Yuwei Chuai, Moritz Pilarski, Thomas Renault, David Restrepo-Amariles, Aurore Troussel-Clément, Gabriele Lenzini, Nicolas Pröllochs
{"title":"Community-based fact-checking reduces the spread of misleading posts on social media","authors":"Yuwei Chuai, Moritz Pilarski, Thomas Renault, David Restrepo-Amariles, Aurore Troussel-Clément, Gabriele Lenzini, Nicolas Pröllochs","doi":"arxiv-2409.08781","DOIUrl":null,"url":null,"abstract":"Community-based fact-checking is a promising approach to verify social media\ncontent and correct misleading posts at scale. Yet, causal evidence regarding\nits effectiveness in reducing the spread of misinformation on social media is\nmissing. Here, we performed a large-scale empirical study to analyze whether\ncommunity notes reduce the spread of misleading posts on X. Using a\nDifference-in-Differences design and repost time series data for N=237,677\n(community fact-checked) cascades that had been reposted more than 431 million\ntimes, we found that exposing users to community notes reduced the spread of\nmisleading posts by, on average, 62.0%. Furthermore, community notes increased\nthe odds that users delete their misleading posts by 103.4%. However, our\nfindings also suggest that community notes might be too slow to intervene in\nthe early (and most viral) stage of the diffusion. Our work offers important\nimplications to enhance the effectiveness of community-based fact-checking\napproaches on social media.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":"12 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Social and Information Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.08781","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Community-based fact-checking is a promising approach to verify social media
content and correct misleading posts at scale. Yet, causal evidence regarding
its effectiveness in reducing the spread of misinformation on social media is
missing. Here, we performed a large-scale empirical study to analyze whether
community notes reduce the spread of misleading posts on X. Using a
Difference-in-Differences design and repost time series data for N=237,677
(community fact-checked) cascades that had been reposted more than 431 million
times, we found that exposing users to community notes reduced the spread of
misleading posts by, on average, 62.0%. Furthermore, community notes increased
the odds that users delete their misleading posts by 103.4%. However, our
findings also suggest that community notes might be too slow to intervene in
the early (and most viral) stage of the diffusion. Our work offers important
implications to enhance the effectiveness of community-based fact-checking
approaches on social media.