RAPID: Dynamic Interactions between Human and Information in Complex Online Environments Responding to SARS-COV-2

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This Rapid Response Research (RAPID) project contributes to fundamental knowledge of risk, crisis communication and behavioral contagion in online environments during a large-scale pandemic outbreak (COVID-19) in the U.S. This project advances knowledge of how health and response agencies can better ensure credible information predominates in social media by quantitatively demonstrating the complex roles of social media in information diffusion during the U.S. COVID-19 pandemic response. Findings will aid in understanding how to reduce the risk of inappropriate behaviors (i.e. not practicing physical distancing) and preventable deaths due to mis- or disinformation and tools developed will enable time-critical tracking of the spreading of accurate and inaccurate information. These findings will support NSF's mission to promote the progress of science and to advance national health and well-being, especially during mission-critical circumstances of major health crises.The research project studies information and human response dynamics in communicating COVID-19 in an online environment, i.e. Twitter. The research identifies key influencers and misinformation sources and examines co-evolution in different information categories over time. Results will help population health agencies and stakeholders better understand how the strategic leveraging of credible information suppresses misinformation and can moderate its adverse consequences. Further, the project reveals how incongruous information may undermine community response goals. The research disentangles the interactive influences of communications between public health agencies, other governmental stakeholders, and the public by examining their social media activities, sentiments, and concerned topics in dynamic information flow networks. Findings will inform future risk communication strategies of virus transmission and prevention. The researchers use system dynamic modeling to investigate reference modes of COVID-19 specific communication. These techniques assess temporal trajectories of credible information and misinformation regarding epidemic-control communication, which in turn, informs the strategic coordination of future risk communication of complex mass casualty events and catastrophic health events such as virulent epidemics and global pandemics.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.