Position title: Assistant Professor
316 Hiram Smith Hall
Office Hours: T 3:30-4:30 pm; or by appointment. Virtual appointments also accepted.
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 UW-Madison Robert & Jean Holtz Center for Science and Technology Studies, the Center for East Asian Studies, and the African Study Programs.
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. Her works have been published in flagship journals across disciplines including American Political Science Review, Journal of Communication, New Media & Society, Public Opinion Quarterly, Public Understanding of Science, Journal of Science Communication, Harvard Kennedy School Misinformation Review, International Public Management Journal, Proceedings of the National Academy of Sciences (PNAS), and among other peer-reviewed journals.
Chen received her Ph.D. in Communication from Stanford University and earned her Master of Public Administration from Columbia University.