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Incentivizing News Consumption on Social Media Platforms Using Large Language Models and Realistic Bot Accounts (2403.13362v3)

Published 20 Mar 2024 in cs.SI, cs.AI, and cs.CL

Abstract: Polarization, declining trust, and wavering support for democratic norms are pressing threats to U.S. democracy. Exposure to verified and quality news may lower individual susceptibility to these threats and make citizens more resilient to misinformation, populism, and hyperpartisan rhetoric. This project examines how to enhance users' exposure to and engagement with verified and ideologically balanced news in an ecologically valid setting. We rely on a large-scale two-week long field experiment (from 1/19/2023 to 2/3/2023) on 28,457 Twitter users. We created 28 bots utilizing GPT-2 that replied to users tweeting about sports, entertainment, or lifestyle with a contextual reply containing two hardcoded elements: a URL to the topic-relevant section of quality news organization and an encouragement to follow its Twitter account. To further test differential effects by gender of the bots, treated users were randomly assigned to receive responses by bots presented as female or male. We examine whether our over-time intervention enhances the following of news media organization, the sharing and the liking of news content and the tweeting about politics and the liking of political content. We find that the treated users followed more news accounts and the users in the female bot treatment were more likely to like news content than the control. Most of these results, however, were small in magnitude and confined to the already politically interested Twitter users, as indicated by their pre-treatment tweeting about politics. These findings have implications for social media and news organizations, and also offer direction for future work on how LLMs and other computational interventions can effectively enhance individual on-platform engagement with quality news and public affairs.

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References (81)
  1. \JournalTitleThe New York Times 10, 2018 (2018).
  2. E Pariser, The Filter Bubble: What the Internet Is Hiding from You. (Penguin Press), (2011).
  3. K Roose, The Making of a YouTube Radical. The New York Times (https://www.nytimes.com/interactive/2019/06/08/technology/youtube-radical.html) (2019).
  4. (2020).
  5. (Cambridge University Press, Cambridge), (2020).
  6. \JournalTitleScience advances 8, eabn9418 (2022).
  7. \JournalTitleJournal of Quantitative Description: Digital Media 1 (2021).
  8. \JournalTitleScience 363, 374–378 (2019).
  9. \JournalTitleNew Media and Society 0 (2021).
  10. \JournalTitleHarvard Kennedy School Misinformation Review 1 (2020).
  11. \JournalTitleProceedings of the National Academy of Sciences 118, e2101967118 (2021).
  12. \JournalTitleScience Advances 9, eadd8080 (2023).
  13. Meta, Widely viewed content report: What people see on facebook (2022) https://transparency.fb.com/data/widely-viewed-content-report/#intro.
  14. \JournalTitleSocial Science Computer Review 35, 33–52 (2017).
  15. \JournalTitlePublic opinion quarterly 80, 298–320 (2016).
  16. \JournalTitleThe International Journal of Press/Politics 28, 601–626 (2023).
  17. AM Guess, (almost) everything in moderation: New evidence on americans’ online media diets. \JournalTitleAmerican Journal of Political Science 65, 1007–1022 (2021).
  18. \JournalTitleScience advances 6, eaay3539 (2020).
  19. \JournalTitleScience Advances 6, 1–6 (2020).
  20. M Prior, News vs. entertainment: How increasing media choice widens gaps in political knowledge and turnout. \JournalTitleAmerican Journal of Political Science 49, 577–592 (2005).
  21. (Yale University Press), (1996).
  22. LM Bartels, Uninformed votes: Information effects in presidential elections. \JournalTitleAmerican journal of political science pp. 194–230 (1996).
  23. \JournalTitleNew Political Science 39, 670–686 (2017).
  24. \JournalTitleAmerican journal of political science pp. 951–971 (2001).
  25. Vol. 30, p. 1 (2006).
  26. E Ekins, The five types of trump voters. \JournalTitleDemocracy Fund Voter Study Group, June. Available (accessed 21 June 2017) at: https://www. voterstudygroup. org/reports/2016-elections/the-five-types-trump-voters (2017) https://www.voterstudygroup.org/publication/the-five-types-trump-voters.
  27. \JournalTitleDepartmental Papers (ASC) p. 2 (2002).
  28. \JournalTitleElements of reason: Cognition, choice, and the bounds of rationality pp. 47–66 (2000).
  29. \JournalTitlePolitical Behavior pp. 1–26 (2023).
  30. \JournalTitleCurrent Directions in Psychological Science 31, 69–75 (2022).
  31. \JournalTitleNew Media & Society 21, 2463–2482 (2019).
  32. \JournalTitleJournal of Communication 72, 345–373 (2022).
  33. \JournalTitleJournalism Studies 21, 459–476 (2020).
  34. N Newman, Reuters institute digital news report 2019. \JournalTitleReuters Institute for the study of Journalism p. 156 (2019).
  35. \JournalTitleDigital Journalism 10, 148–164 (2022).
  36. \JournalTitleInformation, Communication & Society 24, 183–200 (2021).
  37. \JournalTitleSAGE Open 9 (2019).
  38. \JournalTitleMass Communication and Society 23, 455–483 (2020).
  39. \JournalTitlePolitical Communication 0, 1–24 (2022).
  40. \JournalTitlePew Research Center (2021).
  41. WSJ Staff, Inside Tiktok’s Highly Secretive Algorithm (https://www.wsj.com/video/series/inside-tiktoks-highly-secretive-algorithm/investigation-how-tiktok-algorithm-figures-out-your-deepest-desires/) (2021).
  42. \JournalTitleIEEE Transactions on Control of Network Systems 9, 1092–1103 (2021).
  43. \JournalTitleNature pp. 1–8 (2023).
  44. K Andersen, An entrance for the uninterested: Who watches soft news and how does it affect their political participation? \JournalTitleMass Communication and Society 22, 487–507 (2019).
  45. M Prior, Any good news in soft news? the impact of soft news preference on political knowledge. \JournalTitlePolitical Communication 20, 149–171 (2003).
  46. \JournalTitleThe Journal of Politics 68, 946–959 (2006).
  47. \JournalTitleCommunication Research 36, 783–809 (2009).
  48. \JournalTitleMass Communication & Society 8, 111–131 (2005).
  49. M Baum, Soft news and political knowledge: Evidence of absence or absence of evidence? \JournalTitlePolitical Communication 20, 173–190 (2010).
  50. \JournalTitleAmerican Political Science Review 116, 768–774 (2022).
  51. \JournalTitleThe International Journal of Press/Politics 27, 860–886 (2022).
  52. MA Baum, Soft news and political knowledge: Evidence of absence or absence of evidence? \JournalTitlePolitical Communication 20, 173–190 (2003).
  53. \JournalTitlePLoS ONE 1, 173–190 (2021).
  54. \JournalTitleJournalism 0, 14648849221125412 (0).
  55. \JournalTitleNew Media and Society 20, 1282–1302 (2018).
  56. \JournalTitleJournalism 21, 877–895 (2020).
  57. \JournalTitleDigital Journalism 8, 1047–1067 (2020).
  58. S Sobieraj, Credible threat: Attacks against women online and the future of democracy. (Oxford University Press), (2020).
  59. \JournalTitleThe international journal of press/politics 23, 324–344 (2018).
  60. (Association for Computing Machinery, New York, NY, USA), p. 2725–2732 (2020).
  61. \JournalTitlearXiv preprint arXiv:1911.00536 (2019).
  62. V Otero, Ad fontes media’s first multi-analyst content analysis ratings project (2019).
  63. A Chabbra, Political classifier (https://github.com/anshuman23/political_classifier) (2023).
  64. J Hainmueller, Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. \JournalTitlePolitical Analysis 20, 25–46 (2012).
  65. \JournalTitleScientific Reports 10 (2020).
  66. J Robins, A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. \JournalTitleMathematical Modelling 7, 1393–1512 (1986).
  67. (Cambridge University Press), (2022).
  68. \JournalTitlePolitical Communication 41, 129–151 (2024).
  69. \JournalTitleInternational Journal of Press and Politics (2021).
  70. (Cambridge University Press), (1998).
  71. Digital 2023 October Global Statshot Report (https://datareportal.com/reports/digital-2023-october-global-statshot) (2023).
  72. SC McGregor, Social media as public opinion: How journalists use social media to represent public opinion. \JournalTitleJournalism 20, 1070–1086 (2019).
  73. \JournalTitleAmerican Political Science Review 113, 883–901 (2019).
  74. pp. 1–13 (2021).
  75. \JournalTitleProceedings of the National Academy of Sciences 118 (2021).
  76. \JournalTitleAvailable at SSRN 4457400 (2023).
  77. \JournalTitlePolitical Behavior pp. 1–40 (2022).
  78. \JournalTitleScience 381, 398–404 (2023).
  79. \JournalTitleJournal of Community Psychology 49, 838–853 (2021).
  80. Tweepy documentation (https://docs.tweepy.org/en/stable/api.html) (2023).
  81. D Freelon, Geostring (https://github.com/dfreelon/geostring) (2023).
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