Kaiping Chen

Kaiping Chen is an assistant professor in computational communication in the Department of Life Sciences Communication at the University of Wisconsin-Madison and an affiliate of the the Data Science Institute, the Department of Political Science, the UW-Madison Robert & Jean Holtz Center for Science and Technology Studies, the Center for East Asian Studies, the African Studies Programs, Wisconsin Energy Institute, the Nelson Institute for Environmental Studies, and the Institute for Diversity Science.

Chen’s research employs data science and machine learning methods as well as interviews to examine how digital media and technologies affect political accountability to public well-being and how deliberative designs can improve the quality of public discourse on controversial and emerging technologies. Chen’s work is interdisciplinary and draws from theories in communication, political science, and computer science.

Under the first research line, Chen revealed the strategies politicians use to manage and respond to online citizen requests in democratic and authoritarian countries. Chen demonstrated how the promise of digital technology to empower citizens’ voices can be compromised by political interests and information overload. Under the second research line, Chen explores how to empower the lay publics, especially vulnerable populations, to engage in thoughtful discussion on complex policy issues when they are exposed to deliberative communication environments vs organic digital platforms. Chen demonstrated that a deliberative process can foster people’s thoughtful discussion on well-being issues, including food security, sustainable agriculture and environment, and public health. This thoughtful discussion can further increase civic participation in community development.

Chen’s ongoing works study the role of social and group identity in public deliberation and engagement with science issues and misinformation. On one hand, Chen showed that social media posts that use ingroup and outgroup language fuel the spread of misinformation. On the other hand, Chen revealed how social inequalities can be amplified on digital platforms in content creation and sharing.

Chen’s works also contribute to the methodology of studying communication topics by illustrating how to use text as data, visual as data, and social media as data. Her works demonstrate how to integrate qualitative and computational content analyses to examine public discourse, how to synthesize social media discussions with surveys and public deliberation forums to study public opinion, how to use various research tools to collect, analyze and assess Twitter data, and how to combine visual and text data to identify science misinformation.

Chen’s research has been funded by the National Science Foundation, Chan Zuckerberg Initiative, and American Family Insurance. Her works have been published in flagship journals across disciplines including American Political Science Review, Journal of Communication, Journal of Computer-Mediated Communication, Public Opinion Quarterly, New Media & Society, Public Understanding of Science, Harvard Kennedy School Misinformation Review, International Public Management Journal, Proceedings of the National Academy of Sciences (PNAS), and among other peer-reviewed journals. Chen serves as the elected international liaison of the Computational Methods Division at the International Communication Association from 2022-2024. Chen also serves as the elected secretary of the Communicating Science, Health, Environment and Risk Division at the Association for Education in Journalism and Mass Communication. Chen is the recipient of the AEJMC Lillian Lodge Kopenhaver Early Career Woman Scholar Award.

Chen received her Ph.D. in Communication from Stanford University and earned her Master of Public Administration from Columbia University.

You can find a list of her publications on Google Scholar; for more information see her personal website: kaipingchen.com and follow her on Twitter @Kaiping_Chen.