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Kaiping Chen

Assistant Professor of Computational Communication, University of Wisconsin-Madison

About Kaiping

Chen's research focuses on civic engagement and the politics of information. In particular, she examines how different civic engagement methods, such as digital crowdsourcing, online government-citizen forums, deliberative mini-publics, influence the ability of government to collect reliable information from citizens and how these methods can facilitate deliberative political dialogues. She combines computational and experimental methods to collect and analyze large-scale datasets on political activity in the United States, China, and Africa to examine these questions. Besides research, she has done consultant work for local governments in both China and the United States.


"Who Can Deliberate? Reasoning in Deliberate Polls in California and Ghana" (preparing for submission).

Challenges the skeptics on the belief that ordinary citizens cannot reason about politics. Shows that the mass public, even those disadvantaged in socioeconomic status, can reason as substantively as the mass public in the most advanced nations.

"Barriers for Crowd's Impact in Crowdsourced Policymaking: Civic Data Overload and Filter Hierarchy" (with Tanja Altamurto). International Public Management Journal (2018).

Examines the impact of citizen voices in crowdsourced policy making. Points out the big data challenge facing local governments.

"Concealing Corruption: How Chinese Officials Distort Upward Reporting of Online Grievances" (with Jennifer Pan). American Political Science Review 112, no. 3 (2018): 602-620.

Uses machine learning methods to reveal the information manipulation by local governments in reporting public sentiments in China.

"The Value of Crowdsourcing in Public Policymaking: Epistemic, Democratic and Economic Value" (with Tanja Altamurto). The Theory and Practice of Legislation 5, no. 1 (2017): 55-72.

Examines the values of crowdsourcing in public policymaking.