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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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Cited by
Sorted by citations
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Self-citationShifting attention to accuracy can reduce misinformation online
Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., & Rand, D. G. (2021) · Nature · doi:10.1038/s41586-021-03344-2 · Cited by 1,155
Self-citationZhou, J., Zhang, Y., Luo, Q., Parker, A. G., & De Choudhury, M. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581318 · Cited by 288
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Self-citationFact-checker warning labels are effective even for those who distrust fact-checkers
Martel, C., & Rand, D. G. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01973-x · Cited by 74
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Self-citationLu, Z., Li, P., Wang, W., & Yin, M. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555562 · Cited by 68
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BackgroundReview of social science research on the impact of countermeasures against influence operations
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BackgroundResultField Experiments on Social Media
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Self-citationLeveraging Structured Trusted-Peer Assessments to Combat Misinformation
Jahanbakhsh, F., Zhang, A. X., & Karger, D. R. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555637 · Cited by 44
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Self-citationA Model of Online Misinformation
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MethodFighting misinformation among the most vulnerable users
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Self-citationA Comparative Evaluation of Interventions Against Misinformation: Augmenting the WHO Checklist
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Combining refutations and social norms increases belief change
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“The context of online speech is important, where being corrected by an interlocutor raises the degree of language toxicity by the respondent (Mosleh et al. 2021).”
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Mosleh, M., Allen, J. N. L., & Rand, D. G. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2425739122 · Cited by 7
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Self-citationHate Cannot Drive Out Hate: Forecasting Conversation Incivility following Replies to Hate Speech
Yu, X., Blanco, E., & Hong, L. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31422 · Cited by 7
Community Fact-Checks Trigger Moral Outrage in Replies to Misleading Posts on Social Media
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Promoting engagement with social fact-checks onlinePreprint
Mosleh, M., Martel, C., Eckles, D., & Rand, D. G. (2022) · doi:10.31219/osf.io/rckfy · Cited by 5
Self-citationYoon, J., Sathyanarayanan, S., Roesner, F., & Zhang, A. X. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3701209 · Cited by 5
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Straub, V. J., Burton, J. W., Geers, M., & Lorenz-Spreen, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-76948-z · Cited by 4
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Tanaka, Y., Inuzuka, M., Arai, H., Takahashi, Y., Kukita, M., & Inui, K. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580826 · Cited by 6
“Following the procedures used to select and operationally define “true/false” stimuli messages in previous studies [48, 53, 56], we collected fact-checking stories related to COVID19 from third-party fact-checkers (mainichi.”
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Efstratiou, A., Efstratiou, M., Yudhoatmojo, S. B., Blackburn, J., & Cristofaro, E. D. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3687010 · Cited by 6
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Roozenbeek, J., Lasser, J., Marks, M., Qin, T., García, D., Goldberg, B., Debnath, R., van der Linden, S., & Lewandowsky, S. (2025) · Royal Society Open Science · doi:10.1098/rsos.251377 · Cited by 3
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Participatory approaches should be used to address the ethics of social media experiments
Straub, V. J., & Burton, J. W. (2025) · Communications Psychology · doi:10.1038/s44271-025-00204-x · Cited by 4
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Self-citationCombating fake news by empowering fact-checked news spread via topology-based interventionsPreprint
Wang, K., Yaqub, W., Lakhdari, A., & Suleiman, B. (2021) · arXiv.org · doi:10.48550/arxiv.2107.05016 · Cited by 3
“To understand and analyze the fake news phenomenon in social networks, researchers are focusing on detecting, and tracking the spread of false news over a social network [15, 16, 26, 41, 43, 56, 60, 62, 68, 72, 76, 81, 84, 86, 87].”
BackgroundCombining Refutations and Social Norms Increases Belief ChangePreprint
Ecker, U. K. H., Sanderson, J. A., McIlhiney, P., Rowsell, J., Quekett, H., Brown, G. D. A., & Lewandowsky, S. (2022) · doi:10.31234/osf.io/j9w8q · Cited by 3
The Phase Model of Misinformation Interventions
Heuer, H. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711088 · Cited by 2
“Being corrected by human-looking bot accounts, however, is less effective and was shown to decrease the quality of retweets [45] 1.”
The Influence of Content Modality on Perceptions of Online Misinformation
Gunasekara, S., Pareek, S., Kelly, R., & Gonçalves, J. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713098 · Cited by 2
Tanaka, Y., Arai, H., Inuzuka, M., Takahashi, Y., Kukita, M., Iseki, R., & Inui, K. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714243 · Cited by 2
“To define "true/false" stimulus messages, we adhered to established procedures from previous studies [43, 48, 73].”
Zhang, R., & Horvát, E.-Á. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757510 · Cited by 2
“…moderation mechanism called "Community Notes" to replace its original warning label — this allows select users, who must voluntarily sign up as "contributors," on the platform to contest misinformation by providing alternative sources of information that contest the information presented [87].”
Vacunas anticovid y trombosis: el miedo en las redes sociales
García, S. A., Garcinuño, M. I. C., Cano, C. Q., & Pérez, J. C. (2023) · Revista de Comunicación y Salud · doi:10.35669/rcys.2024.14.e307 · Cited by 2
Exploring the impact of automated correction of misinformation in social media
Burel, G., Tavakoli, M. A., & Alani, H. (2024) · AI Magazine · doi:10.1002/aaai.12180 · Cited by 4
“Mosleh et al. (2021) studied the reaction of 2000 users being corrected on Twitter.”
Surjandy, S., & Virtonia, I. (2023) · International Conference on Computer, Control, Informatics and its Applications · doi:10.1109/ic3ina60834.2023.10285785 · Cited by 2
“Multiple studies have attempted to explore why individuals share disinformation; the recipients not knowing any better [8], [9], emotional engagement [9], and the belief that the information is accurate [10].”
BackgroundDetecting Misinformation: Identifying False News Spread by Political Leaders in the Global South
Pereira, F. B., Bueno, N., Nunes, F., Pavão, N. D. C., dos Santos, J. ̃. P. O., & Wirtschafter, V. (2024) · Journal of Quantitative Description Digital Media · doi:10.51685/jqd.2024.007 · Cited by 4
Who is on Twitter (“X”)? Identifying demographic of Twitter usersPreprint
Mosleh, M., & Rand, D. G. (2024) · doi:10.31235/osf.io/wxfcz · Cited by 2
Self-citationNo-Regret Learning Supports Voters’ Competence
Špelda, P., Střítecký, V., & Symons, J. (2023) · Social Epistemology · doi:10.1080/02691728.2023.2252763 · Cited by 1
“It is possible that debunking might produce embarrassment or reduced social standing, and as a result it can be perceived as stigmatising by those who become its subjects (Mosleh et al. 2021).”
InfluentialBelief-consistent information is most shared despite being the least surprising
Goebel, J. T., Susmann, M. W., Parthasarathy, S., Gamal, H. E., Garrett, R., & Wegener, D. T. (2024) · Scientific Reports · doi:10.1038/s41598-024-56086-2 · Cited by 5
Martel, C., Mosleh, M., Eckles, D., & Rand, D. G. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319336 · Cited by 6
“We also note that a follow-up analysis of the data reported in our paper suggests that social corrections may have unfortunate downstream consequences such as increasing the subsequent retweeting of low quality, toxic, or slanted content by the users who get corrected [11].”
InfluentialSelf-citationFujimoto, K., Tanaka, Y., & Inuzuka, M. (2025) · Health Communication · doi:10.1080/10410236.2025.2507676 · Cited by 1
“Given the potential for corrections to make users feel defensive (Mosleh, Martel, et al., 2021), reactance could quite possibly be increased by layering multiple similar interventions.”
Predicting User Tipping in Online Social Networks With Temporal Graph Neural Networks
Kashuv, Y., Alharbi, R., & Thai, M. T. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3588804 · Cited by 1
“This suggests that public corrections may unintentionally reinforce misinformation rather than mitigate it [8].”
Lloyd, T., Nguyen, T., Levy, K. E. C., & Naaman, M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791889 · Cited by 7
“But, all of the platforms that we surveyed still have content moderation systems of some kind, which a CCS complements.”
The cognitive and social dynamics of social correction of misinformation: a scoping review
Vithanage, S. S., Ransom, K., Mendoza, A., & Karunasekera, S. A. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2651115 · Cited by 1
Misinformation in Open and Closed Online Platforms: Impacts and Countermeasures
Butler, L. H., & Ecker, U. K. H. (2023) · Mobile communication in Asia · doi:10.1007/978-94-024-2225-2_15 · Cited by 3
A Fine-Grained Taxonomy of Replies to Hate Speech
Yu, X., Zhao, A., Blanco, E., & Hong, L. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.450 · Cited by 1
“Our findings are consistent with previous work showing that when correcting misstatements, language toxicity increases (Mosleh et al., 2021).”
ResultTIP: Predicting Tipping for User-Centered Misinformation Prevention
Kashuv, Y., Alharbi, R., & Thai, M. T. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-6389-7_8 · Cited by 1
Understanding the efficacy of provenance interventions for tackling misinformationPreprint
Bereskin, C. (2023) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/4a2fw · Cited by 2
Messina, J. P. (2023) · doi:10.1093/oso/9780197581902.002.0003 · Cited by 1
Measuring the misinformation ecosystem
Jiang, S. (2021) · doi:10.17760/d20412317
Free Speech and Non-State Censors
Messina, J. P. (2023) · doi:10.1093/oso/9780197581902.003.0001
Messina, J. P. (2023) · doi:10.1093/oso/9780197581902.003.0003
Messina, J. P. (2023) · doi:10.1093/oso/9780197581902.002.0005
Messina, J. P. (2023) · doi:10.1093/oso/9780197581902.003.0005
The “Old” Media: Censorship and Press Freedom
Messina, J. P. (2023) · doi:10.1093/oso/9780197581902.003.0004
Messina, J. P. (2023) · doi:10.1093/oso/9780197581902.003.0007
Messina, J. P. (2023) · doi:10.1093/oso/9780197581902.003.0006
Countering hate speech: modeling user-generated web content using natural language processing
Yu, X. (2023) · University of North Texas Digital Library (University of North Texas) · doi:10.12794/metadc2179299
Brenes-Peralta, C., Sánchez, R. P., & Siles, I. (2024) · Apuntes Revista de Ciencias Sociales · doi:10.21678/apuntes.96.1985 · Cited by 1
Low-quality news posts receive higher engagement but not more impressions on TwitterPreprint
Mosleh, M., Allen, J. N. L., & Rand, D. G. (2024) · doi:10.31234/osf.io/g2ewb
Self-citationMisinformation in Reels, Influence of Contextual Superimposed Texts in Short Videos
Bartlett, A., Yaqub, W., Suleiman, B., & Mohanty, M. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0567-5_1
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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