论文标题
在Twitter上扩散社区事实检查的错误信息
Diffusion of Community Fact-Checked Misinformation on Twitter
论文作者
论文摘要
在社交媒体上的错误信息传播是一个紧迫的社会问题，平台，决策者和研究人员继续努力。作为对策，最近的作品提议在人群中使用非专家事实检查者来检查事实检查社交媒体内容。尽管实验研究表明，人群可能能够准确评估社交媒体内容的真实性，但了解人们如何缺少人群事实检查（MIS-）信息差异。在这项工作中，我们凭经验分析了误导与不误导社区事实核对帖子的传播。为此，我们使用Twitter的BirdWatch Pilot进行了一个由社区创建的事实检查数据集，并将其映射到在Twitter上重新表演级联。与早期的研究不同的研究分析了在第三方事实检查网站上列出的错误信息的传播（例如，Snopes），我们发现社区事实检查的错误信息的病毒率较小。具体而言，误导帖子估计收到的转发量要少36.62％，而不是误导帖子。部分解释可能在于事实检查目标上的差异：社区事实检查者倾向于与许多关注者有关有影响力的用户帐户的事实检查帖子，而专家事实检查倾向于针对影响力较小的用户共享的帖子。我们进一步发现，不同子类型的错误信息（例如，事实错误，缺失上下文，操纵媒体）的病毒性存在显着差异。此外，我们进行了一项用户研究，以评估（现实世界中）社区创建的事实检查的可靠性。在这里，我们发现用户在很大程度上同意社区创建的事实核对。总的来说，我们的发现提供了有关在研究社交媒体上研究错误信息时的误导与不误导帖子的传播和突出样本选择的关键作用的见解。
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), 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 fact-check 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.
