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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
Open accessAbstract
A prominent approach to combating online misinformation is to debunk false content. Here we investigate downstream consequences of social corrections on users’ subsequent sharing of other content. Being corrected might make users more attentive to accuracy, thus improving their subsequent sharing. Alternatively, corrections might not improve subsequent sharing - or even backfire - by making users feel defensive, or by shifting their attention away from accuracy (e.g., towards various social factors). We identified N=2,000 users who shared false political news on Twitter, and replied to their false tweets with links to fact-checking websites. We find causal evidence that being corrected decreases the quality, and increases the partisan slant and language toxicity, of the users’ subsequent retweets (but has no significant effect on primary tweets). This suggests that being publicly corrected by another user shifts one's attention away from accuracy - presenting an important challenge for social correction approaches.
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“Studies show being corrected could decrease the quality and increases language toxicity in subsequent retweets [76], even when the corrections came from a bot [5].”
From Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions (2023) · cites this paper as [76]
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Metadata
- Authors
- Mosleh, M., Martel, C., Eckles, D., & Rand, D.
- Affiliations
- University of Exeter (GB)
- Massachusetts Institute of Technology (US)
- Venue
- Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2021-05-06
- Pages
- 1-13
- OpenAlex
- W3162145206
- Cited by
- 106
- References
- 51
- Citations by year
- 2026: 5 · 2025: 19 · 2024: 22 · 2023: 30 · 2022: 19 · 2021: 11
- FWCI
- 55.32
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- John Templeton Foundation
- Reset project of Omidyar Group?s Luminate Project Limited
- William and Flora Hewlett Foundation
- Jigsaw
- National Science Foundation Graduate Research Fellowship (174530)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- misinformation debunking, social correction, fact-checking, retweeting behavior, political news, content quality, partisanship, defensive reactions, Twitter, online misinformation
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