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RAPID: Rumor Diffusion During Unrest

RAPID：动乱期间谣言的传播

基本信息

批准号：
2027387
负责人：
Kyounghee Kwon
金额：
$ 7.4万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027387&HistoricalAwards=false
关键词：
RAPID Rumor Diffusion During Unrest

项目摘要

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.

这个项目考察了动乱期间谣言和错误信息的传播。背景是在大规模集体行动中发生新冠疫情的大城市。通过实证研究在这种情况下，谎言是如何被集体行为喂养和喂养的，该项目旨在了解在动荡时期，线上和线下的错误信息和谣言是如何共同演变的。了解动荡时期的谣言传播有助于确定当代传播生态中共识建立的挑战。通过研究大规模背景下的谣言传播，该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、错误信息的变化以及这些信息的共享；将COVID-19谣言插入集体行动叙事；谣言和辟谣信息模式的差异；以及相信错误信息和参与集体行动之间的联系。本文采用了两种方法。首先，采用字符串匹配技术从社交媒体平台抓取的大量数字数据语料库中识别谣言和辟谣信息。包括结构主题建模和扩散树网络分析在内的计算方法用于推断谣言信息中的连贯主题，并从深度、宽度和层间比率方面检查谣言扩散模式。其次，在这两个地区进行在线调查，使用大约1500名匿名参与者的分层样本。运用回归模型来理解不同类型谣言的信念、机构信任和抗议支持之间的关系。该奖项反映了美国国家科学基金会的法定使命，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。