The response to the COVID-19 pandemic may depend critically on how communities of individuals acquire and propagate medical information. This is especially true given the many and frequent updates of medical information transmission, treatment and containment strategies. Although the diffusion of knowledge has been studied previously, very few models incorporate psychologically grounded assumptions about knowledge search, acquisition, and propagation. Specifically, they do not fully consider (1) that information propagation is influenced by the communicative context, (2) that it is critically affected in high-anxiety, high-uncertainty environments, and (3) that results obtained in dyadic level paradigms cannot simply be extrapolated to large social networks. The integration of these advances will address three key questions: First, how does the high perceived risk of infection influence the lay public’s acquisition and propagation of medical information in social networks? Second, how do emotion, social distancing practices, and media consumption impact medical knowledge acquisition and propagation? And third, what socio-cognitive strategies could one employ to increase the spread of accurate information and to diminish the diffusion of misinformation in social networks? Findings from this study could inform the development of infrastructure and practices to better prepare the population for medical crises.
The COVID-19 pandemic provides an urgent opportunity to investigate the acquisition and propagation of medical knowledge in social networks under conditions of high perceived risk of viral infection. This project uses developments in cognitive psychology, social psychology, and network science to address these critical issues. This cross-disciplinary approach will relate individual-level processes to community-wide acquisition of medical knowledge. Online communities will be assembled, and individuals will engage in a sequence of networked conversations about their knowledge of COVID-19. These interactions will occur using a computer-mediated tool, SoPHIE (Software Platform for Human Interaction Experiments), that allows for seamless communication in conversational networks, designed according to experimenter-specified parameters. Individual knowledge will be measured before and after networked conversations, and the conversational network structure will be manipulated together with the threshold at which people are willing to provide and accept information from others. Advances from this project will be of interest to scientists for modeling optimal information diffusion in social networks and to public health officials interested in disseminating accurate information to the public in their efforts to save lives during times of turmoil.
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.
