Community Fact-Checks Do Not Break Follower Loyalty

TL;DR

The paper causally investigates whether authors of misinformation lose followers after their posts are corrected via community-based notes on X. Using a staggered difference-in-differences design and a rich, multi-source longitudinal dataset (3516 posts across 2142 accounts over a 21-day window), the authors estimate group-time ATT effects with a doubly robust approach. Across various model specifications and sub-samples, they find no meaningful, statistically significant declines in follower counts following note display, though small, inconsistent effects appear in some subgroups. The results imply that follower loyalty to spreaders of misinformation is robust to community corrections and highlight the need for complementary interventions beyond follower-level penalties to curb misinformation propagation.

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.

Figures (6)

• Figure 1:
• Figure 2:
• Figure 3:
• Figure 5: Descriptive Statistics of Note Display and Followers. (a) Complementary cumulative distribution function (CCDF) of note display timing, showing that most notes become visible within three days of post publication. (b) CCDF of absolute follower count for treated and never-treated users. (c) CCDF of net follower growth during the 21-day observation window, showing that most users experience minimal change in follower counts. (d) Normalized cumulative follower growth, comparing the growth trajectories over the observation period for treated and never-treated units.
• Figure 6: Estimation results. Shown are coefficient estimates (circles) and their 95% simultaneous confidence intervals (bars) for (a) event-study estimates spanning seven days before and 14.0 after note display, and (b) group-level aggregated estimates, including the simple weighted average across groups.