Sloan School of Management, Massachusetts Institute of Technology.
Hill/Levene Schools of Business, University of Regina.
Perspect Psychol Sci. 2024 Mar;19(2):477-488. doi: 10.1177/17456916231190388. Epub 2023 Aug 18.
Identifying successful approaches for reducing the belief and spread of online misinformation is of great importance. Social media companies currently rely largely on professional fact-checking as their primary mechanism for identifying falsehoods. However, professional fact-checking has notable limitations regarding coverage and speed. In this article, we summarize research suggesting that the "wisdom of crowds" can be harnessed successfully to help identify misinformation at scale. Despite potential concerns about the abilities of laypeople to assess information quality, recent evidence demonstrates that aggregating judgments of groups of laypeople, or crowds, can effectively identify low-quality news sources and inaccurate news posts: Crowd ratings are strongly correlated with fact-checker ratings across a variety of studies using different designs, stimulus sets, and subject pools. We connect these experimental findings with recent attempts to deploy crowdsourced fact-checking in the field, and we close with recommendations and future directions for translating crowdsourced ratings into effective interventions.
确定减少人们对网络错误信息的信任和传播的有效方法非常重要。社交媒体公司目前主要依赖专业的事实核查作为识别虚假信息的主要手段。然而，专业的事实核查在覆盖范围和速度方面存在显著的局限性。在本文中，我们总结了一些研究，这些研究表明，“群体的智慧”可以成功地被利用来帮助大规模识别错误信息。尽管人们可能对普通人评估信息质量的能力存在担忧，但最近的证据表明，汇总普通人或群体的判断可以有效地识别低质量的新闻来源和不准确的新闻帖子：在使用不同设计、刺激集和主体池的各种研究中，群体评分与事实核查者评分高度相关。我们将这些实验结果与最近在该领域尝试部署众包事实核查的尝试联系起来，并在最后提出将众包评分转化为有效干预措施的建议和未来方向。
