MSU researchers secure NSF grant to study emotion, information spread during COVID-19 pandemic

Contact: Sarah Nicholas

STARKVILLE, Miss.—A Mississippi State University research team is using nearly $300,000 from the National Science Foundation to study the intersection of human emotions, information spread and behavior during the COVID-19 pandemic, a project that could inform future health policies.

Portrait of Megan Richardson
Megan Richardson
Portrait of Sujan Ranjan Anreddy
Sujan Ranjan Anreddy
Portrait of Terri Hernandez
Terri Hernandez
Portrait of Christopher Lightsey
Christopher Lightsey

Principal Investigator Megan Richardson and Co-PI Sujan Ranjan Anreddy—both assistant research professors with MSU’s Social Science Research Center—are collaborating with Co-PI and SSRC researcher Terri N. Hernandez, also an assistant professor in the Department of Communication, on the two-year grant from the NSF Behavioral and Cognitive Science, Human Networks and Data Science-Infrastructure. Christopher Lightsey, a research engineer from MSU’s High Performance Computing Collaboratory, is serving as senior personnel on the project.

“This grant will enable us to develop a powerful data visualization tool for exploring the COPE-ID—COVID-19 Online Prevalence of Emotions in Institutions Database. Through this tool, we aim to shed light on the complex interplay of emotions, information spread and human behavior during the COVID-19 pandemic. Our ultimate goal is to improve access to this valuable social media data resource, empowering researchers from various fields to uncover insights that will inform future public health policies and interventions,” Hernandez said.

COPEID graphicThe COPE-ID contains online discussions of COVID-19, including posts about emotions—such as fear and anxiety—and social institutions—such as health care and family. Improving access to data such as this can inform future public health policies and interventions, Hernandez said.

“Users can request samples of data that can be labeled using qualitative or content analysis,” said Hernandez. “This labeled data can then be used to make predictions about future events—predictions that are generated by advanced statistical analyses or machine learning techniques. The tool improves scientists’ access to social media data and allows researchers to test theories of human behavior using user-generated big data.”

This project also is funded by the NSF’s Established Program to Stimulate Competitive Research, or EPSCoR.

For more about the project, visit

For more details about MSU’s College of Arts and Sciences and the Department of Communication, visit and To learn more about MSU’s Social Science Research Center, visit

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