RAPID: Real time monitoring of information consumption regarding the coronavirus

Social, Behavioral and Economic Sciences - The COVID-19 pandemic has highlighted the importance of accurate information as a vehicle for helping the public take needed steps to ensure their health and safety. But social media contain both accurate and inaccurate information. This project will analyze how social media affects the quality of information received by people during the extended crisis. Who receives what information? And in what ways do social media amplify or dampen informational inequalities? The project will build a real-time monitor of information consumption regarding the corona virus, drawn largely from Twitter. Specifically, the project will: (1) build a real time monitor of information regarding the corona virus that would be made available to state and local officials; and (2) evaluate how a medium such as Twitter amplifies/dampens existing informational inequalities around socioeconomic status. The project will focus on identification of misinformation (e.g., ersatz cures) that pose health risks. The project will supply aggregate information to relevant state and local officials regarding the type and quality of information regarding corona virus circulating in their communities, thus informing interventions that public officials can make to combat that misinformation. More generally, the project will identify patterns of information that governmental officials can use to combat misinformation during other extended crises, including those with public health as well as other origins.

Responding appropriately to COVID-19 requires that individuals have accurate information about how it is spread and what they can do to mitigate virus effects. However, misinformation is prevalent, with Twitter being a major source of both accurate and inaccurate information. This project will utilize a matched sample of 1.8 million Twitter handles and voter registration data. The large scale of the data will permit production of reasonable inferences of content sharing at subnational levels?at the state level, and within regions for large states. Because the Twitter data will be linked to voter registration records, and because voter registration data includes information on age, gender, race, partisanship, and address, thus allowing linkage to census tract information, the project will be able to evaluate the relationship between socioeconomic status and information exposure. Further, the project will augment with a survey of about 2000 of the matched data to further examine the factors that affect the quality of information people receive about the corona virus. Findings from the project will inform theories in the social sciences regarding information diffusion, socioeconomic inequality, social media usage, the security of cyberspace, and political differentiation.

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.