This project provides a methodology and theory development that spans the information ecology, dis/mis-information, and human risk behavior in the context of COVID-19. The primary methods use computational models of psychological and cognitive processes, dis/mis-information propagation detection, natural language processing techniques and agent-based modeling to provide a forecasting tool for exploring what-if scenarios and situational assessment.
The information ecology—social media, government messaging, broadcast media—can impact real-world behaviors (e.g., elections, protests). Information can drive attitudes the opinions of individuals. In networks we see this as collective behavior and information bubbles, such as viral memes among other phenomena. In the current pandemic the information ecology not only fluctuates due to changes in what the science is and what policies are implemented, but also has a strong component of competition. Some information streams are pitted against others in a purposeful way.
The study of such an ecology suggests that a scientific understanding of individual’s information consumption and generation is important. But that is not the full picture. Because the information ecology is large and dynamic on the scale of minutes, hours and days, such a scientific understanding of individuals must be valid in the dynamic context of the information ecology. Further, to be useful for policy, planning and operations, this understanding must be useful in computational modeling and simulation for forecasting purposes.