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Understanding Differences in News Article Interaction Patterns on Facebook: Public vs. Private Sharing with Varying Bias and Reliability (2305.11943v2)
Published 19 May 2023 in cs.SI, cs.CY, and cs.HC
Abstract: The rapid growth of news dissemination and user engagement on social media has raised concerns about the influence and societal impact of biased and unreliable information. As a response to these concerns, a substantial body of research has been dedicated to understanding how users interact with different news. However, this research has primarily analyzed publicly shared posts. With a significant portion of engagement taking place within Facebook's private sphere, it is therefore important to also consider the private posts. In this paper, we present the first comprehensive comparison of the interaction patterns and depth of engagement between public and private posts of different types of news content shared on Facebook. To compare these patterns, we gathered and analyzed two complementary datasets: the first includes interaction data for all Facebook posts (private + public) referencing a manually labeled collection of over 19K news articles, while the second contains only interaction data for public posts tracked by CrowdTangle. As part of our methodology, we introduce several carefully designed data processing steps that address some critical aspects missed by prior works but that (through our iterative discussions and feedback with the CrowdTangle team) emerged as important to ensure fairness for this type of study. Our findings highlight significant disparities in interaction patterns across various news classes and spheres. For example, our statistical analysis demonstrates that users engage significantly more deeply with news in the private sphere compared to the public one, underscoring the pivotal role of considering both the public and private spheres of Facebook in future research. Beyond its scholarly impact, the findings of this study can benefit Facebook content moderators, regulators, and policymakers, contributing to a healthier online discourse.
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[2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Thompson, N., Wang, X., Daya, P.: Determinants of news sharing behavior on social media. Journal of Computer Information Systems 60(6), 593–601 (2020) https://doi.org/10.1080/08874417.2019.1566803 Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. CHI ’20, pp. 1–14. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3313831.3376784 Nah and Yamamoto [2018] Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. CHI ’20, pp. 1–14. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3313831.3376784 Nah and Yamamoto [2018] Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. 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[2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. 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[2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. 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Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. CHI ’20, pp. 1–14. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3313831.3376784 Nah and Yamamoto [2018] Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Nah, S., Yamamoto, M.: Communication and Citizenship Revisited: Theorizing Communication and Citizen Journalism Practice as Civic Participation. Communication Theory 29(1), 24–45 (2018) https://doi.org/10.1093/ct/qty019 Jones et al. [2017] Jones, J.J., Bond, R.M., Bakshy, E., Eckles, D., Fowler, J.H.: Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), 1–9 (2017) https://doi.org/10.1371/journal.pone.0173851 Edelson et al. [2021] Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. 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[2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. 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[2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. 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[2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. 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[2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. 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Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. 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[2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. 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[2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. 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[2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. 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[2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. 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[2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. 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Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. 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[2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. 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Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. 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[2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. 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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. 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ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 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Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. 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ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Edelson, L., Nguyen, M.-K., Goldstein, I., Goga, O., McCoy, D., Lauinger, T.: Understanding engagement with u.s. (mis)information news sources on facebook. In: Proceedings of the 21st ACM Internet Measurement Conference. IMC ’21, pp. 444–463. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3487552.3487859 Aldous et al. [2019] Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. 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ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. 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PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. 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In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019). https://doi.org/10.1609/icwsm.v13i01.3208 Brena et al. [2019] Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: A comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019). https://doi.org/10.1609/icwsm.v13i01.3256 Marwick [2018] Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. 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[2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. 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Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. 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[2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. 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Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. 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[2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Marwick, A.E.: Why do people share fake news? a sociotechnical model of media effects. Georgetown law technology review 2(2), 474–512 (2018) Vitak et al. [2011] Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. 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[2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. 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[2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vitak, J., Zube, P., Smock, A., Carr, C.T., Ellison, N., Lampe, C.: It’s complicated: Facebook users’ political participation in the 2008 election. CyberPsychology, behavior, and social networking 14(3), 107–114 (2011) https://doi.org/10.1089/cyber.2009.0226 Mohammadinodooshan and Carlsson [2023] Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. 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[2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. 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Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mohammadinodooshan, A., Carlsson, N.: Effects of political bias and reliability on temporal user engagement with news articles shared on facebook. In: International Conference on Passive and Active Network Measurement (PAM), pp. 160–187. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28486-1_8 Dey et al. [2012] Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. 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[2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. 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[2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. 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[2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. 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[2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. 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Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dey, R., Jelveh, Z., Ross, K.: Facebook users have become much more private: A large-scale study. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 346–352 (2012). https://doi.org/10.1109/PerComW.2012.6197508 Lottridge and Bentley [2018] Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. 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[2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. 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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. 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[2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. 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Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. 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[2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. 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Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lottridge, D., Bentley, F.R.: Let’s hate together: How people share news in messaging, social, and public networks. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI ’18, pp. 1–13. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3173574.3173634 Mellado et al. [2019] Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. 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CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Mellado, C., Moreira, S., Lagos, F., Scherman, A.: Perceived bias, reliability, and journalistic quality in news and social media sharing behavior on facebook: Exploring the mediating role of perceived usefulness. Journalism & Mass Communication Quarterly 96(3), 825–847 (2019) https://doi.org/10.1177/1077699019846412 Guess et al. [2018] Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Guess, A., Nyhan, B., Reifler, J.: Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances 4(1) (2018) https://doi.org/10.1126/sciadv.aau4586 Pennycook et al. [2018] Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. 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[2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. 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[2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. 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Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. 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PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. 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[2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. 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Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. 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Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pennycook, G., Cannon, T.D., Rand, D.G.: Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147(12), 1865 (2018) https://doi.org/10.1037/xge0000465 Horne et al. [2019] Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. 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PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. 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Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. 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ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. 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PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Horne, B.D., Nørregaard, J., Adalı, S.: Different spirals of sameness: A study of content sharing in mainstream and alternative media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 257–266 (2019). https://doi.org/10.1609/icwsm.v13i01.3227 Pidikiti et al. [2020] Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Pidikiti, S., Zhang, J.S., Han, R., Lehman, T., Lv, Q., Mishra, S.: Understanding how readers determine the legitimacy of online news articles in the era of fake news. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’20, pp. 768–775 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381451 Ad Fontes Media [2022] Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. 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[2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. 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ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Ad Fontes Media: Ad Fontes Media Bias Chart. https://adfontesmedia.com/interactive-media-bias-chart/ (2022) Rajapaksha et al. [2018] Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. 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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
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[2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Rajapaksha, P., Farahbakhsh, R., Crespi, N., Defude, B.: Inspecting interactions: Online news media synergies in social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ASONAM ’18, pp. 535–539 (2018). https://doi.org/10.1109/ASONAM.2018.8508534 Media Bias Fact Check [2022] Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. 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[2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. 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CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. 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Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. 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[2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. 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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Media Bias Fact Check: Media Bias Fact Check. https://mediabiasfactcheck.com/ (2022) AllSides [2023] AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 AllSides: AllSides. https://www.allsides.com/ (2023) NewsGuard [2023] NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 NewsGuard: NewsGuard. https://www.newsguardtech.com/ (2023) Haq et al. [2022] Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. 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[2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. 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Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. 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Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. 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[2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. 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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. 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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
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Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Haq, E.-U., Lu, Y.K., Hui, P.: It’s all relative! a method to counter human bias in crowdsourced stance detection of news articles. Proceedings of the ACM on Human-Computer Interaction 6(CSCW), 1–25 (2022) https://doi.org/10.1145/3555636 Huszár et al. [2022] Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Huszár, F., Ktena, S.I., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences 119(1) (2022) https://doi.org/10.1073/pnas.2025334119 Christensen et al. [2020] Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. 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[2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. 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PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Christensen, S.R., Pilling, E.B., Eyring, J., Dickerson, G., Sloan, C.D., Magnusson, B.M.: Political and personal reactions to covid-19 during initial weeks of social distancing in the united states. PloS one 15(9), 1–16 (2020) https://doi.org/10.1371/journal.pone.0239693 Otero [2021] Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Otero, V.: Ad Fontes Media’s Multi-Analyst Content Analysis White Paper (2021). https://adfontesmedia.com/white-paper-2021 CrowdTangle [2021] CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. 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Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. 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[2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. 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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. 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Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. 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Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. 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Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- CrowdTangle: CrowdTangle link checker (2021). https://chromewebstore.google.com/detail/crowdtangle-link-checker/klakndphagmmfkpelfkgjbkimjihpmkh/reviews Tess [2021] Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking (2021) Tess [2022] Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. 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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. 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[2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. 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[2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. 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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Tess: Chrome Extension: Why do the items add up to more than the totals? CrowdTangle Help Center (2022). https://help.crowdtangle.com/en/articles/2252105-chrome-extension-why-do-the-items-add-up-to-more-than-the-totals Meta [2022] Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. 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[2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Meta: URL - Graph API. Facebook for Developers (2022). https://developers.facebook.com/docs/graph-api/reference/v16.0/url Kim and Yang [2017] Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. 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[2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. 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Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Kim, C., Yang, S.-U.: Like, comment, and share on facebook: How each behavior differs from the other. Public Relations Review 43(2), 441–449 (2017) https://doi.org/10.1016/j.pubrev.2017.02.006 Weld et al. [2021] Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Weld, G., Glenski, M., Althoff, T.: Political bias and factualness in news sharing across more than 100,000 online communities. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 796–807 (2021). https://doi.org/10.1609/icwsm.v15i1.18104 Vosoughi et al. [2018] Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) https://doi.org/10.1126/science.aap9559 Altay et al. [2022] Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. 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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. 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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. 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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. 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[2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. 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Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Altay, S., Kleis Nielsen, R., Fletcher, R.: Quantifying the “infodemic”: People turned to trustworthy news outlets during the 2020 coronavirus pandemic. Journal of Quantitative Description: Digital Media 2 (2022) https://doi.org/10.51685/jqd.2022.020 Samory et al. [2020] Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Samory, M., Kesiz Abnousi, V., Mitra, T.: Characterizing the social media news sphere through user co-sharing practices. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 602–613 (2020). https://doi.org/10.1609/icwsm.v14i1.7327 Lamot et al. [2022] Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. 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[2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. 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PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. 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Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Lamot, K., Kreutz, T., Opgenhaffen, M.: “we rewrote this title”: How news headlines are remediated on facebook and how this affects engagement. Social Media + Society 8(3), 20563051221114827 (2022) https://doi.org/10.1177/20563051221114827 Boukes et al. [2022] Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Boukes, M., Chu, X., Noon, M.A., Liu, R., Araujo, T., Kroon, A.C.: Comparing user-content interactivity and audience diversity across news and satire: Differences in online engagement between satire, regular news and partisan news. Journal of Information Technology & Politics 19(1), 98–117 (2022) https://doi.org/10.1080/19331681.2021.1927928 Wischnewski et al. [2021] Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Wischnewski, M., Bruns, A., Keller, T.: Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter. Digital Journalism 9(5), 549–570 (2021) https://doi.org/10.1080/21670811.2021.1903960 González-Bailón et al. [2022] González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., d’Andrea, V., Freelon, D., De Domenico, M.: The advantage of the right in social media news sharing. PNAS Nexus 1(3), 137 (2022) https://doi.org/10.1093/pnasnexus/pgac137 González-Bailón et al. [2023] González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A.M., et al.: Asymmetric ideological segregation in exposure to political news on facebook. Science 381(6656), 392–398 (2023) https://doi.org/10.1126/science.ade713 Maier [2015] Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. Journalism & Mass Communication Quarterly 92(3), 700–722 (2015) https://doi.org/10.1177/1077699015599660 de León and Trilling [2021] León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Maier, S.R.: Compassion fatigue and the elusive quest for journalistic impact: A content and reader-metrics analysis assessing audience response. 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[2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 León, E., Trilling, D.: A sadness bias in political news sharing? the role of discrete emotions in the engagement and dissemination of political news on facebook. Social Media + Society 7(4) (2021) https://doi.org/10.1177/20563051211059710 Eberl et al. [2020] Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. 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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. 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[2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
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- Eberl, J.-M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? how sentiment and issue salience affect users’ emotional reactions on facebook. Journal of Information Technology & Politics 17(1), 48–65 (2020) https://doi.org/10.1080/19331681.2019.1710318 Dewan and Kumaraguru [2017] Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Dewan, P., Kumaraguru, P.: Facebook inspector (fbi): Towards automatic real-time detection of malicious content on facebook. Social Network Analysis and Mining 7, 1–25 (2017) https://doi.org/10.1007/s13278-017-0434-5 Uppada et al. [2022] Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Uppada, S.K., Manasa, K., Vidhathri, B., Harini, R., Sivaselvan, B.: Novel approaches to fake news and fake account detection in osns: user social engagement and visual content centric model. Social Network Analysis and Mining 12(1), 52 (2022) https://doi.org/10.1007/s13278-022-00878-9 Vassio et al. [2022] Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2 Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Vassio, L., Garetto, M., Leonardi, E., Chiasserini, C.F.: Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12(1), 96 (2022) https://doi.org/10.1007/s13278-022-00928-2
- Alireza Mohammadinodooshan (2 papers)
- Niklas Carlsson (11 papers)