About

Project Description

The project provides an enhanced approach to model social behavior during the COVID-19 pandemic in terms of compliance with mitigation strategies, such as mask-wearing, social distancing, and quarantine, using social media data collected from Twitter. Twitter data provide a brief window of opportunity for research on how, and to what extent, the public does or does not comply with the recommended mitigation strategies and public health guidelines.

The research team collects real-time data from social media related to COVID19-exposed regional populations in the U.S. The data are analyzed using machine learning techniques to identify non-mutually exclusive clusters of people based on the similarity of their demographic, geographic, and time information, and establish relationships among clusters. The analyzed data form the basis of a data-driven multi-paradigm simulation model that captures changes in public sentiment over time and quantifies the resistance/compliance with mitigation strategies in terms of demographics, socioeconomic, education, and other characteristics.

The project is funded by an NSF COVID RAPID Awards #2029739 and #2029733, developed as a collaborative proposal led by Auburn University, in collaboration with Columbus State University. The award commenced in May 2020 and ends in April 2021.