Diffusion of Community Fact-Checked Misinformation on Twitter
Chiara Patricia Drolsbach, Nicolas Pröllochs
Abstract
The spread of misinformation on social media is a pressing societal problem that platforms, policymakers, and researchers continue to grapple with. As a countermeasure, recent works have proposed to employ non-expert fact-checkers in the crowd to fact-check social media content. While experimental studies suggest that crowds might be able to accurately assess the veracity of social media content, an understanding of how crowd fact-checked (mis-)information spreads is missing. In this work, we empirically analyze the spread of misleading vs. not misleading community fact-checked posts on social media. For this purpose, we employ a dataset of community-created fact-checks from Twitter's "Birdwatch" pilot and map them to resharing cascades on Twitter. Different from earlier studies analyzing the spread of misinformation listed on third-party fact-checking websites (e. g., snopes.com), we find that community fact-checked misinformation is less viral. Specifically, misleading posts are estimated to receive 36.62 % fewer retweets than not misleading posts. A partial explanation may lie in differences in the fact-checking targets: community fact-checkers tend to factcheck posts from influential user accounts with many followers, while expert fact-checks tend to target posts that are shared by less influential users. We further find that there are significant differences in virality across different sub-types of misinformation (e. g., factual errors, missing context, manipulated media). Moreover, we conduct a user study to assess the perceived reliability of (real-world) community-created fact-checks. Here, we find that users, to a large extent, agree with community-created fact-checks. Altogether, our findings offer insights into how misleading vs. not misleading posts spread and highlight the crucial role of sample selection when studying misinformation on social media.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext ae5cf28d-a594-4bcc-9609-db590a1b51cfCited by top-tier papers11
- Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine MisinformationBing He, Mustaque Ahamad, Srijan KumarWWW 2023 · 62 citations
- Did the Roll-Out of Community Notes Reduce Engagement With Misinformation on X/Twitter?Yuwei Chuai, Haoye Tian, Nicolas Pröllochs, Gabriele LenziniCSCW 2024 · 62 citations
- Supernotes: Driving Consensus in Crowd-Sourced Fact-CheckingSoham De, Michiel A. Bakker, Jay Baxter, Martin SaveskiWWW 2025 · 31 citations
- Which Linguistic Cues Make People Fall for Fake News? A Comparison of Cognitive and Affective ProcessingBernhard Lutz, Marc T. P. Adam, Stefan Feuerriegel, Nicolas Pröllochs et al.CSCW 2024 · 16 citations
- Understanding the Effects of AI-based Credibility Indicators When People Are Influenced By Both Peers and ExpertsZhuoran Lu, Patrick Li, Weilong Wang, Ming YinCHI 2025 · 6 citations
Builds on5
- Birds of a feather don't fact-check each other: Partisanship and the evaluation of news in Twitter's Birdwatch crowdsourced fact-checking programJennifer Allen, Cameron Martel, David G. RandCHI 2022 · 104 citations
- True or False: Studying the Work Practices of Professional Fact-CheckersNicholas Micallef, Vivienne Armacost, Nasir D. Memon, Sameer PatilCSCW 2022 · 71 citations
- Investigating Differences in Crowdsourced News Credibility Assessment: Raters, Tasks, and Expert CriteriaMd Momen Bhuiyan, Amy X. Zhang, Connie Moon Sehat, Tanushree MitraCSCW 2020 · 70 citations
- Will the Crowd Game the Algorithm?: Using Layperson Judgments to Combat Misinformation on Social Media by Downranking Distrusted SourcesZiv Epstein, Gordon Pennycook, David G. RandCHI 2020 · 68 citations
- Political Hashtags & the Lost Art of Democratic DiscourseEugenia Ha Rim Rho, Melissa MazmanianCHI 2020 · 20 citations
Related papers
- "Yeah, this graph doesn't show that": Analysis of Online Engagement with Misleading Data VisualizationsMaxim Lisnic, Alexander Lex, Marina KoganCHI 2024 · 16 citations
- Community Fact-Checks Trigger Moral Outrage in Replies to Misleading Posts on Social MediaYuwei Chuai, Anastasia Sergeeva, Gabriele Lenzini, Nicolas PröllochsCHI 2025 · 5 citations
- 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 ExperimentMohsen Mosleh, Cameron Martel, Dean Eckles, David G. RandCHI 2021 · 109 citations
- Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake NewsNguyen Vo, Kyumin LeeEMNLP 2020 · 4 citations
- Reactions to Fact CheckingD. Scott Appling, Amy S. Bruckman, Munmun De ChoudhuryCSCW 2022 · 13 citations
