Uncovering Authoritarian Rule with Cyber Technology: Estimating the Prevalence of Collective Action and Repression in Authoritarian Regimes with Unstructured Digital Data

Jennifer Pan, John Duchi 2016 - 2018

Uncovering Authoritarian Rule with Cyber Technology: Estimating the Prevalence of Collective Action and Repression in Authoritarian Regimes with Unstructured Digital Data

We aim to develop a methodology to generate the first rigorous scientific measure of a variable of paramount importance to academics and public policy makers worldwide ­­ the prevalence, location, and scale of collective action events and repression of these events in authoritarian regimes. There is on­going debate over whether cyber technologies threaten the survival of authoritarian regimes by facilitating collective action or whether authoritarian regimes are using cyber technologies to strengthen their rule. We propose to develop an independent measure of collective action and the regime’s repressive response to social mobilization by developing algorithms to detect these activities by using unstructured digital data, including images and text, generated by individuals who witness these events and publicly shared on social media platforms.

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Jennifer Pan

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Jennifer Pan

Assistant Professor of Communication, Assistant Professor, by courtesy, of Political Science, and Assistant Professor, by courtesy, of Sociology
Jennifer Pan is an Assistant Professor of Communication, Assistant Professor, by courtesy, of Political Science, Assistant Professor, by courtesy, of Sociology at Stanford University. Her research examines information control and communication in authoritarian regimes to reveal political choices and outcomes in these opaque societies. Much of Pan’s work focuses on China and employs computational methods with large-scale digital data as well as field experiments for causal inference. Pan’s work has appeared in the American Political Science Review, American Journal of Political Science, Comparative Political Studies, and Science. Pan received the 2014 Kellogg/Notre Dame Award for the best paper in comparative politics by the Midwest Political Science Association.
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John Duchi

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John Duchi

Assistant Professor of Statistics and Electrical Engineering and (by courtesy) Computer Science
John Duchi is an assistant professor of Statistics and Electrical Engineering and (by courtesy) Computer Science at Stanford University, with graduate degrees from UC Berkeley and undergraduate degrees from Stanford. His work focuses on large scale optimization problems arising out of statistical and machine learning problems, robustness and uncertain data problems, and information theoretic aspects of statistical learning. He has won a number of awards and fellowships, including a best paper award at the International Conference on Machine Learning, an NSF CAREER award, and a Sloan Fellowship in Mathematics.