RAPID: Cyber-Hostility and COVID-19

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In this project, the new wave of cyber-hostility occasioned by COVID-19 is investigated through exploration and analysis of data from prominent social media sites over a twelve-month period. COVID-19-related cyber-hostility targeting people based on race/ethnicity, age, social class, immigrant status, and political ideology has emerged on social media. Through this project, insights are provided into COVID-19-related cyber-hostility and, more broadly, light is shed on how people respond online to unexpected societal calamities. Findings from this project offer cues on how to communicate with the public during destabilizing times and how to ease tension during crises and stop further escalation of outbursts of cyber-hostility.Data for this project are collected from prominent social media sites, including Twitter, Facebook, Instagram, Snapchat, and Reddit, over a 12-month period. These data allow exploration of how quickly and broadly cyber-hostility related to COVID-19 spreads, both within and between social networks. Machine learning methods are developed to provide a timely and necessary understanding of the sources of COVID-19-related cyber-hostility, how and where it circulates online, and individual and situational factors associated with COVID-19-related cyber-hostility. This analysis allows insight into whether COVID-19-related cyber-hostility develops into a persistent fixture in cyberspace, or becomes less prevalent as the severity of the pandemic diminishes. This project is jointly funded by Sociology, the Established Program to Stimulate Competitive Research (EPSCoR), and Secure and Trustworthy Cyberspace (SaTC).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.