Shmargad, Y. & Klar, S. Sorting the news: how ranking by popularity polarizes our politics. Polit. Commun. 37, 423–446 (2020).
Bavel, J. J. V., Rathje, S., Harris, E., Robertson, C. & Sternisko, A. How social media shapes polarization. Trends Cogn. Sci. 25, 913–916 (2021).
Carpenter, J., Brady, W., Crockett, M., Weber, R. & Sinnott-Armstrong, W. Political polarization and moral outrage on social media. Conn. L. Rev. 52, 1107 (2020).
Wilson, A. E., Parker, V. A. & Feinberg, M. Polarization in the contemporary political and media landscape. Curr. Opin. Behav. Sci. 34, 223–228 (2020).
Brady, W. J., Jackson, J. C., Lindström, B. & Crockett, M. J. Algorithm-mediated social learning in online social networks. Trends Cogn. Sci. https://doi.org/10.1016/j.tics.2023.06.008 (2023).
Braghieri, L., Levy, R. & Trachtman, H. Frictions in news consumption: evidence from social media. Preprint at SSRN https://doi.org/10.2139/ssrn.5494670 (2025).
Brady, W., Jackson, J., Doyle, M. & Baier, S. Engagement-based algorithms disrupt human social norm learning. Preprint at OSF https://doi.org/10.31219/osf.io/mgdwq_v3 (2025).
Centola, D. Change: How to Make Big Things Happen (Little, Brown Spark, 2021).
Guess, A. M. et al. How do social media feed algorithms affect attitudes and behavior in an election campaign? Science 381, 398–404 (2023).
Milli, S. et al. Engagement, user satisfaction, and the amplification of divisive content on social media. PNAS Nexus 4, pgaf062 (2025).
Papakyriakopoulos, O., Serrano, J. C. M. & Hegelich, S. Political communication on social media: a tale of hyperactive users and bias in recommender systems. Online Soc. Netw. Media 15, 100058 (2020).
McClain, C., Widjaya, R., Rivero, G. & Smith, A. The Behaviors and Attitudes of U.S. Adults on Twitter (Pew Research, 2021).
Bail, C. Breaking the Social Media Prism: How to Make Our Platforms Less Polarizing (Princeton Univ. Press, 2021).
Fernbach, P. M. & Van Boven, L. False polarization: cognitive mechanisms and potential solutions. Curr. Opin. Psychol. 43, 1–6 (2022).
Lees, J. & Cikara, M. Inaccurate group meta-perceptions drive negative out-group attributions in competitive contexts. Nat. Hum. Behav. 4, 279–286 (2020).
Brady, W. J. et al. Overperception of moral outrage in online social networks inflates beliefs about intergroup hostility. Nat. Hum. Behav. https://doi.org/10.1038/s41562-023-01582-0 (2023).
Levy, R. Social media, news consumption, and polarization: evidence from a field experiment. Am. Econ. Rev. 111, 831–870 (2021).
Yesilada, M. & Lewandowsky, S. Systematic review: YouTube recommendations and problematic content. Internet Policy Rev. 11, 1652 (2022).
Tucker, J. A. et al. Social media, political polarization, and political disinformation: a review of the scientific literature. Preprint at SSRN https://doi.org/10.2139/ssrn.3144139 (2018).
Terren, L. & Borge-Bravo, R. Echo chambers on social media: a systematic review of the literature. Rev. Commun. Res. 9, 99–118 (2021).
Smith, J. R. & Louis, W. R. Do as we say and as we do: the interplay of descriptive and injunctive group norms in the attitude–behaviour relationship. Br. J. Soc. Psychol. 47, 647–666 (2008).
Burton, J. Algorithmic Amplification for Collective Intelligence (CBS, 2023).
Törnberg, P., Valeeva, D., Uitermark, J. & Bail, C. Simulating social media using large language models to evaluate alternative news feed algorithms. Preprint at https://doi.org/10.48550/arXiv.2310.05984 (2023).
Carmines, E. G., Ensley, M. J. & Wagner, M. W. Who fits the left-right divide? Partisan polarization in the American electorate. Am. Behav. Sci. 56, 1631–1653 (2012).
Huszár, F. et al. Algorithmic amplification of politics on Twitter. Proc. Natl Acad. Sci. USA 119, e2025334119 (2022).
Thorp, H. H. & Vinson, V. Context matters in social media. Science 385, 1393–1393 (2024).
Guess, A. M. et al. Reshares on social media amplify political news but do not detectably affect beliefs or opinions. Science 381, 404–408 (2023).
Nyhan, B. et al. Like-minded sources on Facebook are prevalent but not polarizing. Nature 620, 137–144 (2023).
Krosnick, J. A. & Petty, R. E. in Attitude Strength: Antecedents and Consequences (eds Petty, R. E. & Krosnick, J. A.) 1–24 (Lawrence Erlbaum, 1995).
Luttrell, A. & Sawicki, V. Attitude strength: distinguishing predictors versus defining features. Soc. Pers. Psychol. Compass 14, e12555 (2020).
Gelfand, M. J., Gavrilets, S. & Nunn, N. Norm dynamics: interdisciplinary perspectives on social norm emergence, persistence, and change. Ann. Rev. Psychol. 75, 341–378 (2024).
Bluesky user counter. bsky-users.theo.io https://bsky-users.theo.io/ (accessed 1 September 2024).
Budak, C., Nyhan, B., Rothschild, D. M., Thorson, E. & Watts, D. J. Misunderstanding the harms of online misinformation. Nature 630, 45–53 (2024).
Perspective API. perspectiveapi.com https://perspectiveapi.com/ (accessed 25 September 2024).
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1 (Long and Short Papers) 4171–4186 (Association for Computational Linguistics, 2019).
Finkel, E. J. et al. Political sectarianism in America. Science 370, 533–536 (2020).
Luttrell, A. & Togans, L. J. The stability of moralized attitudes over time. Pers. Soc. Psychol. Bull. 47, 551–564 (2021).
Beknazar-Yuzbashev, G., Jiménez-Durán, R., McCrosky, J. & Stalinski, M. Toxic content and user engagement on social media: evidence from a field experiment. Preprint at SSRN https://doi.org/10.2139/ssrn.5130929 (2025).
Vallone, R. P., Ross, L. & Lepper, M. R. The hostile media phenomenon: Biased perception and perceptions of media bias in coverage of the Beirut massacre. J. Pers. Soc. Psychol. 49, 577–585 (1985).
Matias, J. N. Preventing harassment and increasing group participation through social norms in 2,190 online science discussions. Proc. Natl Acad. Sci. USA 116, 9785–9789 (2019).
Bagchi, C. et al. Social media algorithms can curb misinformation, but do they? Zenodo https://doi.org/10.5281/zenodo.13787981 (2024).
Scott, K. BlueSky benefits from X Brazilian ban as users hit nine million. Tech.co (9 September 2024).
Felicity, T. Bluesky warns of tech outage after usage traffic surges in brazil following X Ban. Venture Capital Post (4 September 2024).
Neely, S. R. Politically motivated avoidance in social networks: a study of Facebook and the 2020 presidential election. Soc. Media Soc. 7, 20563051211055438 (2021).
Boulianne, S. & Larsson, A. O. Engagement with candidate posts on Twitter, Instagram, and Facebook during the 2019 election. N. Media Soc. 25, 119–140 (2023).
Bluesky Social. feed-generator: ATProto Feed Generator Starter Kit. GitHub https://github.com/bluesky-social/feed-generator (2024).
Twitter. Twitter’s recommendation algorithm. blog.x.com https://blog.x.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm (2023).
Zuckerberg, M. et al. Dynamically providing a news feed about a user of a social network. US patent US7669123B2 (2010).
Yang, J. et al. Mixed negative sampling for learning two-tower neural networks in recommendations. In Companion Proceedings of the Web Conference 2020 (eds El Fallah Seghrouchni, A. et al.) 441–447 (Association for Computing Machinery, 2020).
Covington, P., Adams, J. & Sargin, E. Deep neural networks for YouTube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (eds Sen, S. et al.) 191–198 (Association for Computing Machinery, 2016).
Muja, M. & Lowe, D. G. Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36, 2227–2240 (2014).
Liu, T., Moore, A., Yang, K. & Gray, A. An investigation of practical approximate nearest neighbor algorithms. In Advances in Neural Information Processing Systems vol. 17 (eds Saul, L. K., Weiss, Y. & Bottou, L.) https://proceedings.neurips.cc/paper_files/paper/2004/file/1102a326d5f7c9e04fc3c89d0ede88c9-Paper.pdf (MIT Press, 2005).
Quelle, D. & Bovet, A. Bluesky: network topology, polarization, and algorithmic curation. PLoS ONE 20, e0318034 (2025).
Bail, C. A. et al. Exposure to opposing views on social media can increase political polarization. Proc. Natl Acad. Sci. USA 115, 9216–9221 (2018).
Stats for Bluesky by Jaz (jaz.bsky.social). bsky.jazco.dev https://bsky.jazco.dev/stats (accessed 25 September 2024).
Matsunaga, M. Familywise error in multiple comparisons: disentangling a knot through a critique of O’Keefe’s arguments against alpha adjustment. Commun. Methods Meas. 1, 243–265 (2007).
Guess, A., Munger, K., Nagler, J. & Tucker, J. How accurate are survey responses on social media and politics? Polit. Commun. 36, 241–258 (2019).
Brady, W. J. et al. Redesigning algorithms to intervene on social norm misperceptions during a national election [registered report stage 1 protocol]. OSF https://osf.io/c9a3m (2024).

