SCIENCE FACTIONALISM: HOW GROUP IDENTITY LANGUAGE AFFECTS PUBLIC ENGAGEMENT WITH CONTROVERSIAL SCIENCE ON A POPULAR Q&A DIGITAL PLATFORM IN CHINA
Tue 14 September 2021
Speaker: Kaiping Chen, University of Wisconsin-Madison / 11:00am - 12:00pm
Misinformation and outgroup bias language are two pathologies challenging informed citizenship. This paper examines how identity language is used in misinformation and debunking messages about controversial science on a Chinese popular Q&A platform, and their impact on how the public engage with science. We collected an eight-year time series dataset of public discussion (N=40,101) on one of the most controversial science issues in China (GMO) from a popular digital Q&A platform, Zhihu. We found that both misinformation and debunking messages use a substantial amount of group identity languages about a controversial science issue, which we term as the phenomenon of science factionalism – discussion about science is divided by factions that are formed upon science attitudes. We found that posts that use science factionalism receive more digital votes and comments, even among the science-savvy community in China. Science factionalism has consequences on the quality of public discourse, increasing the use of negative language. We discussed the implications of how science factionalism interacts with the digital attention economy to affect public engagement with science misinformation.
Kaiping Chen is an Assistant Professor in Computational Communication at University of Wisconsin-Madison, Department of Life Sciences Communication. She is also a faculty affiliate of the Robert & Jean Holtz Center for Science and Technology Studies, the Center for East Asian Studies, and the African Studies Program. 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 and emerging technologies and mitigate the spread of misinformation and misperception. Chen received Ph.D. in Communication from Stanford University, MPA from Columbia University, and bachelor’s degree in political science and economics from Fudan University. Chen’s work has been supported by the US National Science Foundation. Her work was published or accepted in flagship journals across disciplines, including American Political Science Review, Journal of Communication, New Media & Society, Public Opinion Quarterly, Public Understanding of Science, Proceedings of the National Academy of Sciences (PNAS), and among other peer-reviewed journals.