Events

Dr. Jinyang Zheng
Assistant Professor of Management
Management Information Systems
Krannert School of Management, Purdue University

The centralized misinformation monitoring in social media platforms faces public doubts about controlled speech. Although the democratization of misinformation monitoring is theoretically feasible through crowdsourcing and has been piloted in the field, little is known about its benefit and cost for platforms. This study is the first to explore theoretically the consequence of implementing crowd-based misinformation monitoring in the quality and quantity of users’ content generation, as well as leverage a quasi-field experiment of Twitter’s Birdwatch program to provide causal evidence. With respect to the quality of content, we find an increase in the average cognition in writing, a subjective quality measure, but insignificant changes in the average engagement from other users that reflect quality objectively. The sentiment variance in the created content decreases, implying a reduction in sentiment extremity. These findings, together with a decrease in a machine-learning-calculated probability of misinformation in content, suggest a higher level of performance in misinformation monitoring and, hence, improved average content quality. With respect to the content quantity, the launch of the program decreases the number of tweets created, shortens the average length of tweets, and discourages users from resharing other users’ content, which points to a cost of a lower volume of users’ content generation. A mechanism analysis suggests the average changes in content is at least partially due to a direct effect in terms of the increased quality of content created by users. This study contributes to the literature on crowdsourcing and misinformation and provides significant implications for content platform managers.