I argue that the Chinese government has implemented human-based social grid management and automated surveillance camera system nationwide, which allows the government to detect mobilization attempts and prevent them from becoming collective action. Using large-scale street view image and text data and machine learning, I developed innovative measurements for the density and implementations of surveillance systems. I further utilized a unique dataset of 131,565 protests in China. I found that after the middle of 2013, the number of protests and the average size of protests decreased, fewer violent and disruptive tactics were used by protesters, and the state was less likely to be targeted. Statistical analysis showed that cities that implemented human-based surveillance systems effectively reduced their number and intensity of protests, while prefectures with more surveillance cameras did not see similar decreases.—————————————–Han Zhang is an Assistant Professor in the Division of Social Science at The Hong Kong University of Science and Technology. He obtained his PhD in sociology from Princeton University, and his BS in Computer Science and BA from Peking University. His research interests include computational social science, social movements, and political sociology. His past research has been published in Sociological Methodology, Sociological Methods and Research, Socio-Economic Review, and Chinese Sociological Review. His research won best paper awards from the Section on Collective Behavior and Social Movements of American Sociological Association and the Computational Methods Division of International Communication Association.
Surveillance, Preventive Repression, and Collective Action: Evidence from China
Tuesday, November 29, 2022 - 10:00am to 11:30am
Han Zhang - Assistant Professor, The Hong Kong University of Science and Technology
Free but register in advance