Papers
Topics
Authors
Recent
2000 character limit reached

"Here's Your Evidence": False Consensus in Public Twitter Discussions of COVID-19 Science (2401.13248v3)

Published 24 Jan 2024 in cs.CY and cs.SI

Abstract: The COVID-19 pandemic brought about an extraordinary rate of scientific papers on the topic that were discussed among the general public, although often in biased or misinformed ways. In this paper, we present a mixed-methods analysis aimed at examining whether public discussions were commensurate with the scientific consensus on several COVID-19 issues. We estimate scientific consensus based on samples of abstracts from preprint servers and compare against the volume of public discussions on Twitter mentioning these papers. We find that anti-consensus posts and users, though overall less numerous than pro-consensus ones, are vastly over-represented on Twitter, thus producing a false consensus effect. This transpires with favorable papers being disproportionately amplified, along with an influx of new anti-consensus user sign-ups. Finally, our content analysis highlights that anti-consensus users misrepresent scientific findings or question scientists' integrity in their efforts to substantiate their claims.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (88)
  1. Retracted Articles about COVID-19 Vaccines Enable Vaccine Misinformation on Twitter, 2023.
  2. Trust is the common denominator for COVID-19 vaccine acceptance: A literature review. Vaccine: X, 12:100213, 2022.
  3. Reviewing Interventions to Address Misinformation: The Need to Expand Our Vision Beyond an Individualistic Focus. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW1):87:1–87:34, 2023.
  4. Lies Kill, Facts Save: Detecting COVID-19 Misinformation in Twitter. IEEE Access, 8:155961–155970, 2020.
  5. Birds of a feather don’t fact-check each other: Partisanship and the evaluation of news in Twitter’s Birdwatch crowdsourced fact-checking program. In Proceedings of the Conference on Human Factors in Computing Systems, pages 1–19, 2022.
  6. Alwan, N.A. et al. Scientific consensus on the COVID-19 pandemic: we need to act now. The Lancet, 396(10260):e71–e72, 2020.
  7. Arjun, M.C. et al. Prevalence, characteristics, and predictors of Long COVID among diagnosed cases of COVID-19, 2022. https://www.medrxiv.org/content/10.1101/2022.01.04.21268536v1.
  8. Tweeting From Left to Right: Is Online Political Communication More Than an Echo Chamber? Psychological Science, 26(10):1531–1542, 2015.
  9. Communicating doctors’ consensus persistently increases COVID-19 vaccinations. Nature, 606(7914):542–549, 2022.
  10. Selective and deceptive citation in the construction of dueling consensuses. Science Advances, 2023.
  11. DeSantis administration advises against Covid shots for Florida residents under 65. NBC News, 2023.
  12. Political polarization in US residents’ COVID-19 risk perceptions, policy preferences, and protective behaviors. Journal of Risk and Uncertainty, 61(2):177–194, 2020.
  13. Buchan, S.A. et al. Effectiveness of COVID-19 vaccines against Omicron or Delta symptomatic infection and severe outcomes, 2022. https://www.medrxiv.org/content/10.1101/2021.12.30.21268565v2.
  14. How strong is the association between social media use and false consensus? Computers in Human Behavior, 125:106947, 2021.
  15. Doing your own research and other impossible acts of epistemic superheroism. Philosophical Psychology, 36(5):906–930, 2023.
  16. Institutional Trust and Compliance with Measures to Fight COVID-19. International Advances in Economic Research, 27(1):47–60, 2021.
  17. Cha, M. et al. Prevalence of Misinformation and Factchecks on the COVID-19 Pandemic in 35 Countries: Observational Infodemiology Study. JMIR Human Factors, 8(1):e23279, 2021.
  18. Chen, Z. et al. Efficacy of hydroxychloroquine in patients with COVID-19: results of a randomized clinical trial, 2020. https://www.medrxiv.org/content/10.1101/2020.03.22.20040758v3.
  19. Cialdini, R.B. Descriptive Social Norms as Underappreciated Sources of Social Control. Psychometrika, 72(2):263–268, 2007.
  20. The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), 2021.
  21. Breakthrough SARS-CoV-2 infections in 620,000 U.S. Veterans, February 1, 2021 to August 13, 2021, 2021. https://www.medrxiv.org/content/10.1101/2021.10.13.21264966v1.
  22. Cook, J. et al. Quantifying the consensus on anthropogenic global warming in the scientific literature. Environmental Research Letters, 8(2):024024, 2013.
  23. Getting to the source of the illusion of consensus. Cognition, 223:105023, 2022.
  24. Dorabawila, V. et al. Effectiveness of the BNT162b2 vaccine among children 5-11 and 12-17 years in New York after the Emergence of the Omicron Variant, 2022. https://www.medrxiv.org/content/10.1101/2022.02.25.22271454v1.
  25. Earnshaw, V.A. et al. COVID-19 conspiracy beliefs, health behaviors, and policy support. Translational Behavioral Medicine, 10(4):850–856, 2020.
  26. Misrepresenting Scientific Consensus on COVID-19: The Amplification of Dissenting Scientists on Twitter. arXiv:2111.10594 [physics], 2021.
  27. Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2):488:1–488:35, 2022.
  28. Efstratiou, A. et al. Non-polar Opposites: Analyzing the Relationship between Echo Chambers and Hostile Intergroup Interactions on Reddit. Proceedings of the International AAAI Conference on Web and Social Media, 17:197–208, 2023.
  29. Will the Crowd Game the Algorithm? Using Layperson Judgments to Combat Misinformation on Social Media by Downranking Distrusted Sources. In Proceedings of the CHI Conference on Human Factors in Computing Systems, pages 1–11, 2020.
  30. Farinholt, T. et al. Transmission event of SARS-CoV-2 Delta variant reveals multiple vaccine breakthrough infections, 2021. https://www.medrxiv.org/content/10.1101/2021.06.28.21258780v4.
  31. Garcia-Beltran, W.F. et al. Multiple SARS-CoV-2 variants escape neutralization by vaccine-induced humoral immunity, 2021. https://www.medrxiv.org/content/10.1101/2021.02.14.21251704v2.
  32. Quantifying Controversy on Social Media. ACM Transactions on Social Computing, 1(1):3:1–3:27, 2018.
  33. Gautret, P. et al. Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-label non-randomized clinical trial, 2020. https://www.medrxiv.org/content/10.1101/2020.03.16.20037135v1.
  34. Elusive consensus: Polarization in elite communication on the COVID-19 pandemic. Science Advances, 6(28):eabc2717, 2020.
  35. Grootendorst, M. BERTopic: Neural topic modeling with a class-based TF-IDF procedure, 2022. http://arxiv.org/abs/2203.05794.
  36. Hansen, C.H. et al. Vaccine effectiveness against SARS-CoV-2 infection with the Omicron or Delta variants following a two-dose or booster BNT162b2 or mRNA-1273 vaccination series: A Danish cohort study, 2021. https://www.medrxiv.org/content/10.1101/2021.12.20.21267966v3.
  37. The role and influence of perceived experts in an anti-vaccine misinformation community. medRxiv, page 2023.07.12.23292568, 2023.
  38. Harvey, J.A. et al. Internet Blogs, Polar Bears, and Climate-Change Denial by Proxy. BioScience, 68(4):281–287, 2018.
  39. Havey, N.F. Partisan public health: how does political ideology influence support for COVID-19 related misinformation? Journal of Computational Social Science, 3(2):319–342, 2020.
  40. The differential effects of a governmental debunking campaign concerning COVID-19 vaccination misinformation. Journal of Communication in Healthcare, 16(1):113–121, 2023.
  41. Hughes, J.P. et al. The impact of risk perceptions and belief in conspiracy theories on COVID-19 pandemic-related behaviours. PLOS ONE, 17(2):e0263716, 2022.
  42. Population-level COVID-19 mortality risk for non-elderly individuals overall and for non-elderly individuals without underlying diseases in pandemic epicenters, 2020. https://www.medrxiv.org/content/10.1101/2020.04.05.20054361v2.
  43. Johnson, N.F. et al. The online competition between pro- and anti-vaccination views. Nature, 582(7811):230–233, 2020.
  44. Kata, A. A postmodern Pandora’s box: Anti-vaccination misinformation on the Internet. Vaccine, 28(7):1709–1716, 2010.
  45. Kata, A. Anti-vaccine activists, Web 2.0, and the postmodern paradigm – An overview of tactics and tropes used online by the anti-vaccination movement. Vaccine, 30(25):3778–3789, 2012.
  46. Klein, C. et al. Attention and counter-framing in the Black Lives Matter movement on Twitter. Humanities and Social Sciences Communications, 9(1):1–12, 2022.
  47. Kouzy, R. et al. Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter. Cureus, 12(3):e7255, 2020.
  48. The Measurement of Observer Agreement for Categorical Data. Biometrics, 33(1):159–174, 1977.
  49. Lane, J.C.E. et al. Safety of hydroxychloroquine, alone and in combination with azithromycin, in light of rapid wide-spread use for COVID-19: a multinational, network cohort and self-controlled case series study, 2020. https://www.medrxiv.org/content/10.1101/2020.04.08.20054551v2.
  50. Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1–18, 2021.
  51. Lenzer, J. Covid-19: Group of UK and US experts argues for “focused protection” instead of lockdowns. 371, 2020.
  52. Your opinion on climate change might not be as common as you think. Nature Climate Change, 3(4):334–337, 2013.
  53. Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nature Human Behaviour, 5(3):337–348, 2021.
  54. Characterizing and Comparing COVID-19 Misinformation Across Languages, Countries and Platforms. In Companion Proceedings of the Web Conference 2021, pages 213–223, 2021.
  55. Metaxas, P.T. et al. Do Retweets indicate Interest, Trust, Agreement? (Extended Abstract), 2014. http://arxiv.org/abs/1411.3555.
  56. Perverse Downstream Consequences of Debunking: Being Corrected by Another User for Posting False Political News Increases Subsequent Sharing of Low Quality, Partisan, and Toxic Content in a Twitter Field Experiment. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, number 182, pages 1–13. 2021.
  57. Erroneous Consonance. How inaccurate beliefs about physician opinion influence COVID-19 vaccine hesitancy. Vaccine, 41(12):2093–2099, 2023.
  58. Oreskes, N. The Scientific Consensus on Climate Change. Science, 306(5702):1686–1686, 2004.
  59. Merchants of doubt: How a handful of scientists obscured the truth on issues from tobacco smoke to global warming. Bloomsbury Publishing USA, 2011.
  60. Papadamou, K. et al. “It Is Just a Flu”: Assessing the Effect of Watch History on YouTube’s Pseudoscientific Video Recommendations. Proceedings of the International AAAI Conference on Web and Social Media, 16:723–734, 2022.
  61. Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention. Psychological Science, 31(7):770–780, 2020.
  62. Overconfidently conspiratorial: Conspiracy believers are dispositionally overconfident and massively overestimate how much others agree with them, 2022. https://psyarxiv.com/d5fz2/.
  63. Prasad, A. Anti-science Misinformation and Conspiracies: COVID–19, Post-truth, and Science & Technology Studies (STS). Science, Technology and Society, 27(1):88–112, 2022.
  64. Rajasingham, R. et al. Hydroxychloroquine as pre-exposure prophylaxis for COVID-19 in healthcare workers: a randomized trial, 2020. https://www.medrxiv.org/content/10.1101/2020.09.18.20197327v1.
  65. Conspiracy theories as barriers to controlling the spread of COVID-19 in the U.S. Social Science & Medicine, 263:113356, 2020.
  66. Roozenbeek, J. et al. Susceptibility to misinformation about COVID-19 around the world. Royal Society Open Science, 7(10):201199, 2020.
  67. The “false consensus effect”: An egocentric bias in social perception and attribution processes. Journal of Experimental Social Psychology, 13(3):279–301, 1977.
  68. We Are the People and You Are Fake News: A Social Identity Approach to Populist Citizens’ False Consensus and Hostile Media Perceptions. Communication Research, 47(2):201–226, 2020.
  69. Media and social media attention to retracted articles according to Altmetric. PLOS ONE, 16(5):e0248625, 2021.
  70. Moral Emotions Shape the Virality of COVID-19 Misinformation on Social Media. In Proceedings of the ACM Web Conference 2022, pages 3706–3717, 2022.
  71. Disinformation as Collaborative Work: Surfacing the Participatory Nature of Strategic Information Operations. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW):1–26, 2019.
  72. Drawing the Lines of Contention: Networked Frame Contests Within #BlackLivesMatter Discourse. Proceedings of the ACM on Human-Computer Interaction, 1(CSCW):96:1–96:23, 2017.
  73. Motivated Skepticism in the Evaluation of Political Beliefs. American Journal of Political Science, 50(3):755–769, 2006.
  74. Echo Chambers on Social Media: A Systematic Review of the Literature. Review of Communication Research, 9:99–118, 2021.
  75. Trust and Reliance in Consensus-Based Explanations from an Anti-Misinformation Agent. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, pages 1–7, 2023.
  76. Using Expert Sources to Correct Health Misinformation in Social Media. Science Communication, 39(5):621–645, 2017.
  77. Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW1):108:1–108:32, 2023.
  78. Democratic Norms, Social Projection, and False Consensus in the 2020 U.S. Presidential Election. Journal of Political Marketing, 0(0):1–14, 2021.
  79. Wojcieszak, M. False Consensus Goes Online: Impact of Ideologically Homogeneous Groups on False Consensus. Public Opinion Quarterly, 72(4):781–791, 2008.
  80. Wojcieszak, M. Deliberation and Attitude Polarization. Journal of Communication, 61(4):596–617, 2011.
  81. Wong, J.C. Hydroxychloroquine: how an unproven drug became Trump’s coronavirus ‘miracle cure’. https://www.theguardian.com/world/2020/apr/06/hydroxychloroquine-trump-coronavirus-drug, 2020.
  82. World Health Organization. WHO Coronavirus (COVID-19) Dashboard, 2023. https://covid19.who.int.
  83. Prevalence of Low-Credibility Information on Twitter During the COVID-19 Outbreak. arXiv:2004.14484 [cs], 2020.
  84. The Illusion of Consensus: A Failure to Distinguish Between True and False Consensus. Psychological Science, 30(8):1195–1204, 2019.
  85. Different roles of interpersonal trust and institutional trust in COVID-19 pandemic control. Social Science & Medicine, 293:114677, 2022.
  86. Understanding the Use of e-Prints on Reddit and 4chan’s Politically Incorrect Board. In Proceedings of the 15th ACM Web Science Conference 2023, pages 117–127, 2023.
  87. Zhang, Y. et al. Shifting Trust: Examining How Trust and Distrust Emerge, Transform, and Collapse in COVID-19 Information Seeking. In Proceedings of the CHI Conference on Human Factors in Computing Systems, pages 1–21, 2022.
  88. Zollo, F. et al. Debunking in a world of tribes. PLOS ONE, 12(7):e0181821, 2017.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Slide Deck Streamline Icon: https://streamlinehq.com

Whiteboard

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

Sign up for free to view the 12 tweets with 27 likes about this paper.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up for Free