Can Crowdchecking Curb Misinformation? Evidence from Community Notes
COM3 Level 1
SR12, COM3 01-21
Abstract:
To battle against rampant misinformation on social media, many platforms are experimenting with crowdsourced fact-checking—systems that rely on social media users' annotations of potentially misleading content. This paper investigates the efficacy of such systems in curbing misinformation in the context of Community Notes, a pioneering crowdsourced fact-checking system from Twitter/X. Utilizing a regression discontinuity design, we empirically identified the positive effect of publicly displaying community notes on an author's voluntary retraction of the noted tweet, demonstrating the viability of crowdsourced fact-checking as an alternative to professional fact-checking and forcible content removal. Our findings reveal that the effect is primarily driven by the author's reputational concern and perceived social pressure, and there is considerable heterogeneity of such effect depending on specific tweet- and user-level characteristics. Platforms, therefore, can exploit the underlying mechanism and explore the use of contextual factors to harness the full potential of crowdsourced fact-checking. Furthermore, results from discrete-time survival analyses show that publicly displaying community notes not only increases the probability of tweet retractions but also, accelerates the retraction process among retracted tweets, thereby improving platforms' responsiveness to curb misinformation. This study offers important insights to both social media platforms and policymakers on the promise of crowdsourced fact-checking and calls for the broad participation of social media users to collectively tackle the problem of misinformation.
Here are links to National Press Club interviews featuring Prof. Rui’s research.
https://www.press.org/newsroom
https://www.press.org/newsroom/crowd-checking-online-misinformation-might-actually-work-research-shows
Bio:
Huaxia Rui is the Xerox Chair Professor at Simon Business School and an affiliated faculty at Goergen Institute for Data Science and Artificial Intelligence, at The University of Rochester. He is broadly interested in AI, economics, and social media, and has published in premier academic journals such as Information Systems Research, Management Science, MIS Quarterly, Journal of Financial Economics, Journal of Mathematical Economics, Journal of Management Information Systems, and Production and Operation Management. His research has been covered in media such as Financial Times, The Wall Street Journal, Bloomberg, Yahoo! News, and LSE Business Review. He received his Ph.D. from UT Austin and his bachelor's degree from Tsinghua University.