BWL XI: Two papers accepted at WWW
Two new research papers have been accepted for publication in the Proceedings of the ACM Web Conference (WWW '26). WWW is a premier publication outlet in data science with a low acceptance rate (CORE Ranking A*).
Paper 1: Bobek M, Pröllochs N (2026)
Community Fact-Checks Do Not Break Follower Loyalty
Proceedings of the ACM Web Conference (WWW), forthcoming.
Abstract: Major social media platforms increasingly adopt community-based fact-checking to address misinformation on their platforms. While previous research has largely focused on its effect on engagement (e.g., reposts, likes), an understanding of how fact-checking affects a user's follower base is missing. In this study, we employ quasi-experimental methods to causally assess whether users lose followers after their posts are corrected via community fact-checks. Based on time-series data on follower counts for N=3516 community fact-checked posts from X, we find that community fact-checks do not lead to meaningful declines in the follower counts of users who post misleading content. This suggests that followers of spreaders of misleading posts tend to remain loyal and do not view community fact-checks as a sufficient reason to disengage. Our findings underscore the need for complementary interventions to more effectively disincentivize the production of misinformation on social media.
Preprint available via arXiv
Paper 2: Chuai Y, Lenzini G, Pröllochs N (2026)
Consensus Stability of Community Notes on X
Proceedings of the ACM Web Conference (WWW), forthcoming.
Abstract: Community-based fact-checking systems, such as Community Notes on X (formerly Twitter), aim to mitigate online misinformation by surfacing annotations judged helpful by contributors with diverse viewpoints. While prior work has shown that the platform’s bridging-based algorithm effectively selects helpful notes at the time of display, little is known about how evaluations change after notes become visible. Using a large-scale dataset of 437,396 Community Notes and 35 million ratings from over 580,000 contributors, we examine the stability of helpful notes and the rating dynamics that follow their initial display. We find that 30.2% of displayed notes later lose their helpful status and disappear. Using interrupted time series models, we further show that note display triggers a sharp increase in rating volume and a significant shift in rating leaning, but these effects differ across rater groups. Contributors with viewpoints similar to note authors tend to increase supportive ratings, while dissimilar contributors increase negative ratings, producing systematic post-display polarization. Counterfactual analyses suggest that this post-display polarization, particularly from dissimilar raters, plays a substantial role in note disappearance. These findings highlight the vulnerability of consensus-based fact-checking systems to polarized rating behavior and suggest pathways for improving their resilience.