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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Being corrected could decrease the quality and increase language toxicity in subsequent retweets, even when the corrections came from a bot.
“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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“However, due to lack of data access, researchers have little insight into how stakeholder behavior changes after online field experiments (Mosleh, Pennycook, & Rand, 2021), with one field experiment demonstrating backfire effects after corrections (Mosleh, Martel, et al., 2021).”
BackgroundSeo, H., Xiong, A., Lee, S., & Lee, D. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19344 · Cited by 21 · see citing works
“While some studies showed that correction can significantly reduce participants’ misinformation belief (Vraga and Bode 2017; Bode and Vraga 2018), the other studies showed negative effects such as backfire (Nyhan and Reifler 2010; Mosleh et al. 2021), or combined effects simultaneously (Nyhan and Reifler 2015; Jiang a…”
Lisnic, M., Lex, A., & Kogan, M. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642448 · Cited by 19 · see citing works
“Even though interventions are often successful in their goal of correcting people’s beliefs, researchers have described the potential for fact-checking to have a backfre efect : to solidify incorrect beliefs [41] and to increase toxicity [33].”
He, L., & He, C.-Y. (2022) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3555138 · Cited by 18 · see citing works
“There is a rich stream of research that investigates various ways of fact-checking and debunking information and presenting corrected information to internet users [12,42].”
Background'Debunk-It-Yourself': Health Professionals Strategies for Responding to Misinformation on TikTok
Sharevski, F., Loop, J. V., Jachim, P., Devine, A., & Das, S. (2024) · New Security Paradigms Workshop · doi:10.1145/3703465.3703469 · Cited by 15 · see citing works
“But being directly corrected by another user in the comments section of the platform, evidence shows, decreases the quality and increases the partisan slant and language toxicity of the users’ subsequent content posts [53].”
Reinero, D. A., Harris, E., Rathje, S., Duke, A., & Bavel, J. J. V. (2023) · doi:10.31234/osf.io/z4df3 · Cited by 14 · see citing works
Appling, S., Bruckman, A., & de Choudhury, M. (2022) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3555128 · Cited by 13 · see citing works
“A study by [29] performed experimentation with an intervention to automatically reply to a misinformation poster that what they shared is inaccurate.”
BackgroundHaber, J. R., Singh, L., Budak, C., Josh Pasek, B., Balan, M., Callahan, R., Churchill, R., Herren, B., & Kawintiranon, K. (2021) · Harvard Kennedy School Misinformation Review · doi:10.37016/mr-2020-84 · Cited by 14 · see citing works
Field experiments on social mediaPreprint
Mosleh, M., Pennycook, G., & Rand, D. G. (2021) · doi:10.31234/osf.io/dgmc2 · Cited by 12 · see citing works
Self-citationHeiss, R., Nanz, A., Knupfer, H., Engel, E., & Matthes, J. (2023) · New Media & Society · doi:10.1177/14614448231209946 · Cited by 11 · see citing works
“Mosleh et al. (2021) conducted a field experiment in which participants who posted incorrect political news received a correction message from human-looking bot accounts with a link to a fact-checking website.”
Johnson, R. G., Al-khateeb, S., Forbes, A., & Cupido, K. (2023) · Communication & Sport · doi:10.1177/21674795231213330 · Cited by 10 · see citing works
“Even with this limitation, researchers have extensively studied the nature of toxic discourse on 4Chan (Petit, 2022), Reddit (Almerekhi et al., 2019; DeCook, 2022; Massanari, 2017; O ’ Hallarn et al., 2023), and X (Fan et al., 2021; Kim et al., 2022; Mosleh et al., 2021; Saveski et al., 2021).”
BackgroundWho Shares Fake News? Uncovering Insights from Social Media Users’ Post Histories
Schoenmueller, V., Blanchard, S. J., & Johar, G. V. (2024) · Journal of Marketing Research · doi:10.1177/00222437241281873 · Cited by 17 · see citing works
Miyazaki, K., Uchiba, T., Tanaka, K., An, J., Kwak, H., & Sasahara, K. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22176 · Cited by 10 · see citing works
Kim, J., Wang, Z., Shi, H., Ling, H.-K., & Evans, J. A. (2025) · Nature Communications · doi:10.1038/s41467-025-55868-0 · Cited by 8 · see citing works
“Earlier research on motivated reasoning suggests that misinformation tags contradicting targeted poster ’ s beliefs could back fi re and reinforce preexisting beliefs 16,17 , which could discourage people from exploring diverse information 18 .”
Shusas, E., & Forte, A. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3650801 · Cited by 8 · see citing works
“In the last few years, we have seen the detrimental effects of misin-formation disrupting people’s ability to access reliable information related to topics such as health [11], elections [13], wars [23], and politics [20].”
BackgroundAlper, S., Toribio‐Flórez, D., Capraro, V., & Douglas, K. M. (2024) · Social Psychological and Personality Science · doi:10.1177/19485506241302878 · Cited by 9 · see citing works
Individual drivers of toxicity in radical right-wing populist legislative campaigns
Daniel, W. T., Frelin, E., Robert, M.-V., & Rowley-Abel, L. (2024) · West European Politics · doi:10.1080/01402382.2024.2311040 · Cited by 7 · see citing works
“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).”
Yaqub, W., Kay, J., & Goldwater, M. B. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637335 · Cited by 7 · see citing works
“Our work also builds on the large body of work on analysing and understanding the social and cognitive mechanisms contributing to the exponential spread of false news [53, 66, 91].”
BackgroundKhatiwada, P., Halko, L., Syed, N., Mahesh, A., Alvanpour, A., & Mauriello, M. L. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711071 · Cited by 7 · see citing works
“Fact-checks Fact-checks also struggle to reach audiences, fade over time, and may lead users to share more low-quality content [46, 73, 106].”
Mosleh, M., Allen, J. N. L., & Rand, D. G. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2425739122 · Cited by 5 · see citing works
“To measure the quality of content shared by the users in our sample, we followed a standard practice in the literature and used the reliability of the publisher as a proxy for accuracy of content (20, 21, 44, 45).”
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 5 · see citing works
Community Fact-Checks Trigger Moral Outrage in Replies to Misleading Posts on Social Media
Chuai, Y., Sergeeva, A., Lenzini, G., & Pröllochs, N. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713909 · Cited by 7 · see citing works
“Additionally, the corrections and moral judgments from peer users may backfire and make authors of misleading posts feel defensive, promoting their subsequent shares of low-quality, partisan, and toxic content [83].”
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 · see citing works
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 · see citing works
“Past work has also shown that corrections may not actually be very effective [15] and can even potentially backfire [30].”
Public attitudes towards social media field experiments
Straub, V. J., Burton, J. W., Geers, M., & Lorenz-Spreen, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-76948-z · Cited by 4 · see citing works
Who Does Not Benefit from Fact-checking Websites?
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 4 · see citing works
“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.”
MethodShusas, E. (2024) · Designing Interactive Systems Conference · doi:10.1145/3656156.3665126 · Cited by 3 · see citing works
“In recent years, we have seen the injurious efects of mis-information disrupting the ability of people to access reliable information related to topics such as health [12], elections [15], wars [24], and politics [22].”
Background"Here's Your Evidence": False Consensus in Public Twitter Discussions of COVID-19 Science
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 3 · see citing works
“E.g., people over-whelmingly target political opponents when correcting misin-formation [5], but being corrected leads to elevated political bias in the content that a user shares [56].”
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 2 · see citing works
Is Resistance Futile? Citizen Knowledge, Motivated Reasoning, and Fact-Checking
Szewach, P., Reifler, J., & Oscarsson, H. E. (2022) · Routledge eBooks · doi:10.4324/9781003111474-9 · Cited by 3 · see citing works
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 3 · see citing works
Solutions and Challenges for Addressing Misinformation
Martel, C., & Rand, D. G. (2025) · Journal of Public Policy & Marketing · doi:10.1177/07439156251352415 · Cited by 3 · see citing works
Self-citationCombating fake news by empowering fact-checked news spread via topology-based interventionsPreprint
Wang, K., Yaqub, W., Lakhdari, A., & Suleiman, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05016 · Cited by 3 · see citing works
“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 · see citing works
The Phase Model of Misinformation Interventions
Heuer, H. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711088 · Cited by 2 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
“…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 · see citing works
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 3 · see citing works
“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 · see citing works
“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 2 · see citing works
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 · see citing works
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 · see citing works
“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 1 · see citing works
Martel, C., Mosleh, M., Eckles, D., & Rand, D. G. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319336 · Cited by 6 · see citing works
“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 · see citing works
“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 · see citing works
“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 3 · see citing works
“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 · see citing works
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 1 · see citing works
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 · see citing works
“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 · see citing works
Understanding the efficacy of provenance interventions for tackling misinformationPreprint
Bereskin, C. (2023) · doi:10.31219/osf.io/4a2fw · Cited by 1 · see citing works
Messina, J. P. (2023) · doi:10.1093/oso/9780197581902.002.0003 · Cited by 1 · see citing works
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
Brenes-Peralta, C., Sánchez, R. P., & Siles, I. (2024) · Apuntes Revista de Ciencias Sociales · doi:10.21678/apuntes.96.1985
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
- 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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