Dr. Megan Stubbs-Richardson is an Assistant Research Professor at the Social Science Research Center (SSRC) of Mississippi State University. She directs the Data Science for Social Sciences Laboratory at the SSRC. She is leading one of the laboratory’s missions to create open-source social media-based infrastructure projects to broaden participation in social media research. Megan has been a PI or Co-PI on projects funded by the National Science Foundation (NSF), the National Institute of Justice (NIJ), the Bureau of Justice Assistance (BJA), and the Department of Defense (DoD). She has been conducting social media data analyses at the SSRC since 2012. In her research, she uses digital data to identify crime patterns and trends alongside examining digital data’s role in crime prevention. She serves or has served as PI for the COPE-ID Data Viz projects, which initially provided almost 15 million posts about how people coped during the pandemic on 10 social media platforms. The database is now paired with the data visualization and analytics tool, allowing users to draw down data samples more easily and blend social and computational scientific research methods to examine the data.
https://orcid.org/0000-0001-8636-497X
Contribution to Project: PI Dr. Stubbs-Richardson (Sociology Social Scientist) directs the Data Science for Social Sciences (DS3) laboratory and has worked with social media data since 2012. Stubbs-Richardson often collaborates with SBE scientists and computer scientists to access and integrate data science and social science methods across large volumes of social media data to examine various social problems. For this initiative, she has provided oversight of the two awards (NSF #2031246 and #2318438) while also leading the development of the user guides for the COPE-ID’s database and data visualization tool and associated methods, such as the topic modeling user guides. She is also working toward broadening research participation in social media research by sharing presentations with potential users and then evaluating the effectiveness of the COPE-ID data visualization tool.