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Molly Roberts

Margaret E. (Molly) Roberts is an associate professor of political science at UC San Diego. Her research interests lie in the intersection of political methodology and the politics of information, with a specific focus on methods of automated content analysis and the politics of censorship in China. She received a PhD from Harvard in government (2014), MS in statistics from Stanford (2009), and BA in international relations and economics (2009). Currently, she is working on a variety of projects that span censorship, propaganda, topic models and other methods of text analysis. Her work has appeared or is forthcoming in the American Journal of Political Science, American Political Science Review, and Political Analysis.
  • Censorship
  • China
  • Politics of information
  • Political methodology
  • Propaganda 

Hobbs, William, and Margaret E. Roberts. 2018. "How Sudden Censorship Can Increase Access to Information." American Political Science Review 112 (3): 621–36.

King, Gary, Jennifer Pan, and Margaret E. Roberts. 2017. "How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument." American Political Science Review 111 (3): 484–501.

King, Gary, Jennifer Pan, and Margaret E. Roberts. 2014. "Reverse Engineering Chinese Censorship: Randomized Experimentation and Participant Observation." Science 345 (6199): 110.

King, Gary, Jennifer Pan, and Margaret E. Roberts. 2013. "How Censorship in China Allows Government Criticism but Silences Collective Expression." American Political Science Review 107 (2): 326–43.

King, Gary, and Margaret E. Roberts. 2015. "How Robust Standard Errors Expose Methodological Problems They Do Not Fix." Political Analysis 23 (2):15979.

Roberts, Margaret E., Brandon M. Stewart, and Edo M. Airoldi. 2016. "A Model of Text for Experimentation in the Social Sciences." Journal of the American Statistical Association 111 (515): 988–1003.

Roberts, Margaret E., Brandon Stewart, and Dustin Tingley. 2016. "Navigating the Local Modes of Big Data: The Case of Topic Models." In Computational Social Science: Discovery and Prediction, edited by R. Michael Alvarez, New York: Cambridge University Press.