RAPID: Rumor Diffusion During Unrest

这个项目研究了在动乱期间谣言和错误信息的传播。背景是一个大城市，在大规模集体行动中爆发了COVID-19疫情。 通过实证研究在这种情况下，虚假信息是如何滋生的，以及集体行为是如何滋生的，该项目旨在了解在线和离线的错误信息和谣言在动荡时期是如何共同演变的。了解动荡时期的谣言传播有助于识别当代传播生态中建立共识的挑战。通过研究大规模背景下的谣言传播，这项研究增加了了解当局如何使用错误信息的价值。该项目将对培训未来的从业人员如何处理有关动乱的新闻产生影响。研究问题涉及谣言、错误信息以及这些信息的共享;将COVID-19谣言插入集体行动叙述;谣言和谣言揭穿信息模式的差异;以及错误信息信念与集体行动参与之间的关联。 采取了两种方法。首先，采用字符串匹配技术从社交媒体平台抓取的大型数字数据库中识别谣言和谣言揭穿消息。计算方法，包括结构化的主题建模和扩散树网络分析被用来推断连贯的主题在谣言消息，并检查谣言扩散模式的深度，宽度和层间比率。第二，在两个区域进行在线调查，使用约1 500名匿名参与者的分层抽样。通过回归建模，了解不同类型谣言中的信念、机构信任和抗议支持之间的关系。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project examines diffusion of rumors and misinformation during unrest. The context is a large city where the COVID-19 outbreak has happened amid a large-scale collective action. By empirically examining how falsehoods feed and are fed by collective behaviors in this situation, the project aims to understand how misinformation and rumors both online and offline co-evolve during a period of unrest. Understanding rumor diffusion during unrest contributes to identifying challenges for consensus building in contemporary communication ecology. By studying rumor diffusion in a large-scale context, the study adds value in knowing how authorities use misinformation. The project will have impact on training of future practitioners in terms of how to deal with news about unrest. Research questions concern variation in rumors, misinformation, and the sharing of these; interpolation of COVID-19 rumors into collective action narratives; differences in the patterns of rumors and rumor-debunking messages; and the association between beliefs in misinformation and participation in collective action. Two methodological approaches are taken. First, string-matching techniques are employed to identify rumors and rumor-debunking messages from a large corpus of digital data, crawled from social media platforms. Computational methods including structural topic modeling and diffusion tree network analysis are used to infer coherent themes across rumor messages and to examine rumor diffusion patterns in terms of depth, width, and interlayer ratios. Second, online surveys are conducted in both regions using a stratified sample of about 1,500 anonymous participants. Regression modeling is performed to understand relationships among beliefs in different types of rumors, institutional trust, and protest support.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.