In 2020, Social Science Matrix and D-Lab were jointly awarded a grant from the Social Science Research Council’s Social Data Research and Dissertation Fellowship program to pursue a year-long initiative entitled “Solidarity and Strife: Democracies in a Time of Pandemic.”
Focusing on social media datasets culled from Twitter, Reddit, and YouTube, this project takes a comparative approach to assessing the impact of the COVID-19 pandemic on political polarization and solidarity in the United States and the United Kingdom, two nations governed by populist leaders who initially denied the seriousness of the viral outbreak.
Under the mentorship of the principal investigators — Marion Fourcade, Professor of Sociology at UC Berkeley and Director of Social Science Matrix; David Harding, Professor of Sociology at UC Berkeley and Director of D-Lab; and Claudia von Vacano, Executive Director of D-Lab and Digital Humanities at Berkeley — five graduate students (the Social Media Research Fellows) received a stipend to develop research projects making use of these specific social media datasets.
To support the project and help the graduate students realize their own research projects, D-Lab supplied a data-intensive training program, while Social Science Matrix organized a cross-disciplinary seminar series, presented below, that sets the production and analysis of social media data in a broader ethical and political economy perspective. The group is also working in close collaboration with Gregory Renard and Sandrine Chausson, two industry researchers who have contributed to develop an innovative methodology for value analysis using natural language processing (NLP) on top of unstructured data.
Recorded on January 29, 2021, this video features Joshua A. Tucker, Professor of Politics, affiliated Professor of Russian and Slavic Studies, and affiliated Professor of Data Science at New York University. In his lecture, Tucker discussed his recent research, which focused on understanding how well can ordinary people do in identifying the veracity of news in real time. Using a unique research design, Tucker compared the performance of both ordinary citizens and professional fact checkers in identifying fake news, and gained insights into the individual-level characteristics of those likely to incorrectly identify false news stories as true. He also researched what interventions can reduce the prevalence of this behavior, and the prospects for crowdsourcing to serve as a viable means for identifying false news stories in real time. He also reported preliminary findings from a replication of this study focusing exclusively on news about COVID-19.
Recorded on February 19, 2021, this video features a lecture by Joan Donovan, Research Director for the Shorenstein Center on Media, Politics and Public Policy at Harvard University. Dr. Donovan leads the field in examining internet and technology studies, online extremism, media manipulation, and disinformation campaigns. Dr. Donovan leads The Technology and Social Change Project (TaSC), which explores how media manipulation is a means to control public conversation, derail democracy, and disrupt society. TaSC conducts research, develops methods, and facilitates workshops for journalists, policy makers, technologists, and civil society organizations on how to detect, document, and debunk media manipulation campaigns. Dr. Donovan’s research and teaching interests are focused on media manipulation, effects of disinformation campaigns, and adversarial media movements.