RAPID: Agenda Generality and Behavior in Social Network Interactions about COVID-19

The same types of social networks that transmit the COVID-19 disease may be leveraged to spread healthy norms and positive behaviors. This research gathers important, time-sensitive data to understand the conditions under which digital social networks can influence health behaviors relevant to the COVID-19 pandemic and how to reduce negative social influences in digital environments. At a time when people spend unprecedented amounts of time on digital networks, public health strategies deployed in these networks may shape the health and social outcomes of Americans in the next 12 months. This research advances understanding of these public health strategies.

The project?s theory is that discussing either general or specific issues (e.g., curbing COVID-19 disease or wearing a mask) can have important consequences on the spread of risky attitudes and behaviors through a network. The research entails (a) an ecological study of Twitter and Instagram networks and (b) experiments manipulating the mix of healthy and risky behaviors promoted in the network and the focus of the discussion on either general or specific issues. The project generates public health recommendations and algorithms to improve health discussions on social media. The investigators use a dynamic panel data model to predict individual behavior from the individual?s own attitudes and own past behaviors as well as the behaviors of other members of their network. The research team uses graph convolutional networks both to capture richer network aspects and to model sparse networks.

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.