RAPID: Rumor Diffusion During Unrest

Social, Behavioral and Economic Sciences - 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.