Welcome! I’m an Associate Professor in the Department of Political Science at the University of California, San Diego. My 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.
I received a PhD from Harvard in Government (2014), MS from Stanford in Statistics (2009) and BA from Stanford in International Relations and Economics (2009). Much of my research uses blogs, online experiments, and large collections of newspaper articles to understand the influence of censorship and propaganda on the spread of information in China.
Currently, I’m working on a variety of additional projects that span censorship, propaganda, topic models, and other methods of text analysis. Some of this work has appeared or is forthcoming in the American Journal of Political Science, American Political Science Review, Science, and Political Analysis. My book, Censored: Distraction and Diversion Inside China’s Great Firewall was published by Princeton University Press in 2018.
Censored: Distraction and Diversion Inside China's Great Firewall (2018, Princeton University Press) describes how incomplete and porous censorship in China have an impact on information consumption in China, even when censorship is easy to circumvent. Using new methods to measure the influence of censorship and propaganda, I present a theory that explains how censorship impacts citizens' access to information and in turn why authoritarian regimes decide to use different types of censorship in different circumstances to control the spread of information.
Naoki Egami, Christian Fong, Justin Grimmer, Margaret Roberts and Brandon Stewart. 2017. "How to Make Causal Inferences with Text."
Ben Liebman, Margaret Roberts, Rachel Stern and Alice Wang. 2017. "Mass Digitization of Chinese Court Cases: How to Use Text as Data in the Field of Chinese Law."
Roberts, Margaret E, Brandon M. Stewart and Richard Nielsen. “Matching Methods for High-Dimensional Data with Applications to Text.”
The Structural Topic Model: R package stm for estimating the Structural Topic Model.
The Structural Topic Model Browser: R package stmBrowser for visualizing the Structural Topic Model.