Data visualizations like “Flatten the Curve” have shaped the political discourse around public health responses to Covid-19 in crucial ways. Experts are using visualizations to track the spread of the virus, and these graphs have proven critical for encouraging people to practice public health protocols like social distancing and wearing masks. However, as thousands of charts flood online media, there is a pressing need to study how they are being created, disseminated, and understood in order to mitigate confusion and the spread of misinformation. This project seeks to combine methods in computer science, computational social science, and digital ethnography to study how conversations about data—what numbers we can trust, and what visualizations should be critiqued—unfold in discussions about the coronavirus and about public policy. How are users on Twitter and Reddit rhetorically deploying visualizations to frame arguments about public health, economic recovery, and the role of data in policy making? How can data and their visual representations be manipulated online to support different policies?
By taking a mixed methods approach to understanding how data visualizations about Covid-19 circulate, this project allows us to introduce interpretivist frameworks and methodologies to computer science while leveraging computational power to answer qualitative questions about media ecologies on Twitter and Reddit. Indeed, this sociotechnical approach to analyzing the online dissemination of data visualizations will become especially critical with the upcoming 2020 presidential election and future discussions about a coronavirus vaccine.s
PhD Candidate, Massachusetts Institute of Technology