Renault Thomas, Mosleh Mohsen, Rand David G
Maison des Sciences Économiques, University Paris 1 Panthéon-Sorbonne, CNRS, Centre d'Economie Sorbonne, Paris 75013, France.
Oxford Internet Institute, University of Oxford, Oxford OX1 3JS, United Kingdom.
Proc Natl Acad Sci U S A. 2025 Jun 24;122(25):e2502053122. doi: 10.1073/pnas.2502053122. Epub 2025 Jun 16.
We use crowd-sourced assessments from X's Community Notes program to examine whether there are partisan differences in the sharing of misleading information. Unlike previous studies, misleadingness here is determined by agreement across a diverse community of platform users, rather than by fact-checkers. We find that 2.3 times more posts by Republicans are flagged as misleading compared to posts by Democrats. These results are not base rate artifacts, as we find no meaningful overrepresentation of Republicans among X users. Our findings provide strong evidence of a partisan asymmetry in misinformation sharing which cannot be attributed to political bias on the part of raters, and indicate that Republicans will be sanctioned more than Democrats even if platforms transition from professional fact-checking to Community Notes.
我们使用来自X的社区笔记计划的众包评估来研究在分享误导性信息方面是否存在党派差异。与以往的研究不同，这里的误导性是由平台用户的多元化群体达成的共识决定的，而不是由事实核查人员决定。我们发现，与民主党人发布的帖子相比，共和党人发布的帖子被标记为误导性的次数多出2.3倍。这些结果并非基础概率假象，因为我们发现在X用户中，共和党人并没有显著的过多占比。我们的研究结果提供了强有力的证据，证明在错误信息分享方面存在党派不对称，这不能归因于评分者的政治偏见，并且表明即使平台从专业事实核查转向社区笔记，共和党人受到的制裁也会比民主党人更多。
