Welcome! I’m an Associate Professor in the Department of Political Science and the Halıcıoğlu Data Science Institute at the University of California, San Diego. I co-direct the China Data Lab at the 21st Century China Center. I am also part of the Omni-Methods Group. 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 and propaganda 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 social media, online experiments, and large collections of texts to understand the influence of censorship and propaganda on access to information and beliefs about politics.
My book, Censored: Distraction and Diversion Inside China’s Great Firewall, published by Princeton University Press in 2018, was listed as one of the Foreign Affairs Best Books of 2018, was honored with the Goldsmith Book Award, and has been awarded the Best Book Award in the Human Rights Section and Information Technology and Politics Section of the American Political Science Association. I am honored to hold a Chancellor’s Associates Endowed Chair at UCSD.
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."
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.