RAPID: Rumor Diffusion During Unrest

这个项目考察了谣言和错误信息在动乱期间的传播。背景是一个大城市，新冠肺炎疫情发生在一场大规模的集体行动中。通过经验性地研究在这种情况下集体行为是如何助长和助长谎言的，该项目旨在了解错误信息和谣言是如何在线上和线下在动荡时期共同演变的。了解谣言在动乱期间的传播有助于识别当代传播生态中建立共识的挑战。通过在大规模背景下研究谣言传播，这项研究增加了了解当局如何使用错误信息的价值。该项目将对未来从业者如何处理动乱消息方面的培训产生影响。研究问题涉及谣言、错误信息和这些信息的分享；新冠肺炎谣言在集体行动叙事中的插入；谣言和揭穿谣言的模式的差异；以及对错误信息的信念与参与集体行动之间的关联。采取了两种方法论方法。首先，使用字符串匹配技术从从社交媒体平台抓取的大量数字数据中识别谣言和揭穿谣言消息。使用包括结构主题建模和扩散树网络分析在内的计算方法来推断谣言消息中连贯的主题，并根据深度、宽度和层间比率来检查谣言传播模式。其次，在线调查是在两个地区进行的，采用分层抽样的方式，约有1500名匿名参与者。回归模型被用来理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。这个奖项反映了NSF的法定使命，并通过使用基金会的智力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.