Kaiping Chen

Assistant Professor of Computational Communication, University of Wisconsin-Madison

Areas of Expertise:

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About Kaiping

Chen's research employs data science and machine learning methods as well as interviews to examine how digital media and technologies affect politicians' accountability to public well-being and how deliberative designs can improve the quality of public discourse on controversial & emerging technologies and mitigate the spread of misinformation.

Contributions | No Jargon Podcast | In the News | Publications

Contributions

How Digital Contributions from Citizens Can Help U.S. Public Officials Govern Effectively

Key Findings Brief, March 2019

Publications

"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.