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

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.

Publications:

Pan, J., & Chen, K. (2018). Concealing Corruption: How Chinese Officials Distort Upward Reporting of Online Grievances. American Political Science Review,1-19. doi: 10.1017/S0003055418000205
King, G., Pan, J., & Roberts, M. (2017). How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument. American Political Science Review, 111(3), 484-501. doi:10.1017/S0003055417000144
Jaros, K and Pan, J, (2017). “China's Newsmakers: Official Media Coverage and Political Shifts in the Xi Jinping Era.” China Quarterly.
Chen, J., Pan, J. and Xu, Y. (2016), “Sources of Authoritarian Responsiveness: A Field Experiment in China.” American Journal of Political Science, 60: 383–400. doi:10.1111/ajps.12207
Pan, J. Forthcoming. "How Market Dynamics of Domestic and Foreign Social Media Firms Shape Strategies of Internet Censorship." Problems of Post-Communism. (PDF) http://www.jenpan.com/jen_pan/market.pdf
Gary King, 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, Pp. 484-501. Publisher's Version Copy at http://j.mp/2pGQ843
Pan, Jennifer and Xu, Yiqing, China's Ideological Spectrum (March 7, 2017). Forthcoming, The Journal of Politics. Available at SSRN: https://ssrn.com/abstract=2593377 or http://dx.doi.org/10.2139/ssrn.2593377

Researchers

https://comm.stanford.edu/faculty-pan/

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.

https://stanford.edu/~jduchi/

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.