Argyle, L. P. et al. Leveraging AI for democratic discourse: chat interventions can improve online political conversations at scale. Proc. Natl Acad. Sci. USA 120, e2311627120 (2023).
Coeckelbergh, M. Why AI Undermines Democracy and What to Do About It (Polity, 2024).
Epstein, Z. et al. Art and the science of generative AI. Science 380, 1110–1111 (2023).
Jungherr, A. Artificial intelligence and democracy: a conceptual framework. Soc. Media Soc. https://doi.org/10.1177/20563051231186353 (2023).
Kreps, S. & Kriner, D. How AI threatens democracy. J. Democr. 34, 122–131 (2023).
Summerfield, C. et al. The impact of advanced AI systems on democracy. Nat. Hum. Behav. https://doi.org/10.1038/s41562-025-02309-z (2025).
Kalla, J. L. & Broockman, D. E. The minimal persuasive effects of campaign contact in general elections: evidence from 49 field experiments. Am. Polit. Sci. Rev. 112, 148–166 (2018).
Coppock, A., Hill, S. J. & Vavreck, L. The small effects of political advertising are small regardless of context, message, sender, or receiver: evidence from 59 real-time randomized experiments. Sci. Adv. 6, eabc4046 (2020).
Hewitt, L. How experiments help campaigns persuade voters: evidence from a large archive of campaigns’ own experiments. Am. Polit. Sci. Rev. 118, 2021–2039 (2024).
Capraro, V. et al. The impact of generative artificial intelligence on socioeconomic inequalities and policy making. PNAS Nexus 3, pgae191 (2024).
Hackenburg, K., Ibrahim, L., Tappin, B. M. & Tsakiris, M. Comparing the persuasiveness of role-playing large language models and human experts on polarized U.S. political issues. AI Soc. https://doi.org/10.1007/s00146-025-02464-x (2025).
Hanley, H. W. A. & Durumeric, Z. Machine-made media: monitoring the mobilization of machine-generated articles on misinformation and mainstream news websites. In Proc. International AAAI Conference on Web and Social Media 542–556 (ICWSM, 2024).
Matz, S. C. et al. The potential of generative AI for personalized persuasion at scale. Sci Rep. 14, 4692 (2024).
Hackenburg, K. et al. Scaling language model size yields diminishing returns for single-message political persuasion. Proc. Natl Acad. Sci. USA 122, e2413443122 (2025).
Simon, F. M., Altay, S. & Mercier, H. Misinformation reloaded? Fears about the impact of generative AI on misinformation are overblown. Harvard Kennedy School Misinformation Review https://misinforeview.hks.harvard.edu/article/misinformation-reloaded-fears-about-the-impact-of-generative-ai-on-misinformation-are-overblown/ (2023).
Kalla, J. L. & Broockman, D. E. Reducing exclusionary attitudes through interpersonal conversation: evidence from three field experiments. Am. Polit. Sci. Rev. 114, 410–425 (2020).
Kalla, J. L. & Broockman, D. E. Which narrative strategies durably reduce prejudice? Evidence from field and survey experiments supporting the efficacy of perspective-getting. Am. J. Polit. Sci. 67, 185–204 (2023).
Salvi, F., Horta Ribeiro, M., Gallotti, R. & West, R. On the conversational persuasiveness of GPT-4. Nat. Hum. Behav. 9, 1645–1653 (2025).
Hackenburg, K. et al. The levers of political persuasion with conversational AI. Science https://doi.org/10.1126/science.aea3884 (2025).
Costello, T. H., Pennycook, G. & Rand, D. G. Durably reducing conspiracy beliefs through dialogues with AI. Science 385, eadq1814 (2024).
Costello, T. H., Pennycook, G. & Rand, D. Just the facts: how dialogues with AI reduce conspiracy beliefs. Preprint at https://osf.io/preprints/psyarxiv/h7n8u (2025).
Boissin, E., Costello, T. H., Spinoza-Martín, D., Rand, D. G. & Pennycook, G. Dialogues with large language models reduce conspiracy beliefs even when the AI is perceived as human. PNAS Nexus 4, pgaf325 (2025).
Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).
Storey, J. D. A direct approach to false discovery rates. J. R. Statist. Soc. B 64, 479–498 (2002).
Clifton, L. & Clifton, D. A. The correlation between baseline score and post-intervention score, and its implications for statistical analysis. Trials 20, 43 (2019).
Secretary of the Commonwealth of Massachusetts. The Natural Psychedelic Substances Act. The General Court of the Commonwealth of Massachusetts https://malegislature.gov/Bills/193/H4255.pdf (2024).
Tappin, B. M., Berinsky, A. J. & Rand, D. G. Partisans’ receptivity to persuasive messaging is undiminished by countervailing party leader cues. Nat. Hum. Behav. 7, 568–582 (2023).
Wittenberg, C., Tappin, B. M., Berinsky, A. J. & Rand, D. G. The (minimal) persuasive advantage of political video over text. Proc. Natl Acad. Sci. USA 118, e2114388118 (2021).
Flynn, D. J., Nyhan, B. & Reifler, J. The nature and origins of misperceptions: understanding false and unsupported beliefs about politics. Polit. Psychol. 38, 127–150 (2017).
Kahan, D. M. Ideology, motivated reasoning, and cognitive reflection: an experimental study. Judgm. Decis. Mak. 8, 407–424 (2013).
Taber, C. S. & Lodge, M. Motivated skepticism in the evaluation of political beliefs. Am. J. Polit. Sci. 50, 755–769 (2006).
Thaler, M. The fake news effect: experimentally identifying motivated reasoning using trust in news. Am. Econ. J. Microecon. 16, 1–38 (2024).
Kubin, E., Puryear, C., Schein, C. & Gray, K. Personal experiences bridge moral and political divides better than facts. Proc. Natl Acad. Sci. USA 118, e2008389118 (2021).
Bimber, B. & de Zúñiga, H. G. Social influence and political participation around the world. Eur. Political Sci. Rev. 14, 135–154 (2022).
Cialdini, R. B. The science of persuasion. Sci. Am. 284, 76–81 (2001).
Cohen, G. L. Party over policy: the dominating impact of group influence on political beliefs. J. Pers. Soc. Psychol. 85, 808–822 (2003).
Green, M. C. & Brock, T. C. The role of transportation in the persuasiveness of public narratives. J. Pers. Soc. Psychol. 79, 701–721 (2000).
Lau, R. R. & Rovner, I. B. Negative campaigning. Annu. Rev. Polit. Sci. 12, 285–306 (2009).
Galasso, V., Nannicini, T. & Nunnari, S. Positive spillovers from negative campaigning. Am. J. Polit. Sci. 67, 5–21 (2023).
Riet, J. V., Schaap, G. & Kleemans, M. Fret not thyself: the persuasive effect of anger expression and the role of perceived appropriateness. Motiv. Emotion 42, 103–117 (2017).
Walter, N., Tukachinsky, R., Pelled, A. & Nabi, R. Meta-analysis of anger and persuasion: an empirical integration of four models. J. Commun. 69, 73–93 (2019).
Petty, R. E. & Cacioppo, J. T. Communication and Persuasion (Springer, 1986)
Pennycook, G. A. A framework for understanding reasoning errors: from fake news to climate change and beyond. Adv. Exp. Soc. Psychol. 67, 131–208 (2023).
Feinberg, M. & Willer, R. The moral roots of environmental attitudes. Psychol. Sci. 24, 56–62 (2013).
Cacioppo, J. T., Petty, R. E. & Kao, C. F. Central and peripheral routes to persuasion: an individual difference perspective. J. Pers. Soc. Psychol. 51, 1032–1043 (1986).
Wheeler, S. C., Petty, R. E. & Bizer, G. Y. Self-schema matching and attitude change: situational and dispositional determinants of message elaboration. J. Consum. Res. 31, 787–797 (2005).
Argyle, L. P. et al. Testing theories of political persuasion using AI. Proc. Natl Acad. Sci. USA 122, e2412815122 (2025).
Garrett, R. K. & Bond, R. M. Conservatives’ susceptibility to political misperceptions. Sci. Adv. 7, eabf1234 (2021).
González-Bailón, S. et al. Asymmetric ideological segregation in exposure to political news on Facebook. Science 381, 392–398 (2023).
Guess, A., Nagler, J. & Tucker, J. Less than you think: prevalence and predictors of fake news dissemination on Facebook. Sci. Adv. 5, eaau4586 (2019).
Lasser, J. et al. Social media sharing of low-quality news sources by political elites. PNAS Nexus 1, pgac186 (2022).
Lasser, J. et al. From alternative conceptions of honesty to alternative facts in communications by US politicians. Nat. Hum. Behav. 7, 2140–2151 (2023).
Mosleh, M., Yang, Q., Zaman, T., Pennycook, G. & Rand, D. G. Differences in misinformation sharing can lead to politically asymmetric sanctions. Nature 634, 609–616 (2024).
Renault, T., Mosleh, M. & Rand, D. Republicans are flagged more often than Democrats for sharing misinformation on X’s Community Notes. Proc. Natl Acad. Sci. USA 122, e2502053122 (2025).
Mummolo, J. & Peterson, E. Demand effects in survey experiments: an empirical assessment. Am. Polit. Sci. Rev. 113, 517–529 (2019).
Friedman, J. H., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Soft. 33, 1–22 (2010).
Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. B 58, 267–288 (1996).
Lin, H. Replication data for political persuasion using human-AI dialogues. Harvard Dataverse https://doi.org/10.7910/DVN/DODEXZ (2025).