Community Notes Moderate Engagement With and Diffusion of False Information Online

2025-02-18

AI Summary

This study investigates the causal impact of fact-checking interventions—such as X’s Community Notes—on the dissemination of misinformation on online social platforms. Leveraging a time-series dataset of over 40,000 labeled posts, it applies synthetic control methods for causal inference, circumventing the infeasibility of randomized experiments in real-world settings. Results show that Community Notes significantly suppress user engagement and information diffusion: immediate post-labeling reductions include 45.7% fewer retweets, 43.5% fewer likes, 22.9% fewer replies, and 14.0% fewer views; cumulative lifetime reductions are 11.4% (retweets), 13.0% (likes), 7.3% (replies), and 5.7% (views). A key contribution is identifying the differential regulatory mechanism: Community Notes primarily curtail propagation depth (cascade length) rather than breadth (initial exposure), offering an interpretable, quantifiable empirical foundation for platform-level misinformation governance.

Technology Category

• Application Domains: Misinformation & Fake News
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Reasoning under Uncertainty: Causality

Application Category

• Responsible Web: Mitigating misinformation and disinformation
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Social networks scaffold the diffusion of information on social media. Much attention has been given to the spread of true vs. false content on online social platforms, including the structural differences between their diffusion patterns. However, much less is known about how platform interventions on false content alter the engagement with and diffusion of such content. In this work, we estimate the causal effects of Community Notes, a novel fact-checking feature adopted by X (formerly Twitter) to solicit and vet crowd-sourced fact-checking notes for false content. We gather detailed time series data for 40,074 posts for which notes have been proposed and use synthetic control methods to estimate a range of counterfactual outcomes. We find that attaching fact-checking notes significantly reduces the engagement with and diffusion of false content. We estimate that, on average, the notes resulted in reductions of 45.7% in reposts, 43.5% in likes, 22.9% in replies, and 14.0% in views after being attached. Over the posts' entire lifespans, these reductions amount to 11.4% fewer reposts, 13.0% fewer likes, 7.3% fewer replies, and 5.7% fewer views on average. In reducing reposts, we observe that diffusion cascades for fact-checked content are less deep, but not less broad, than synthetic control estimates for non-fact-checked content with similar reach. This structural difference contrasts notably with differences between false vs. true content diffusion itself, where false information diffuses farther, but with structural patterns that are otherwise indistinguishable from those of true information, conditional on reach.

Problem

• Community Notes reduce false content engagement
• Fact-checking decreases false information diffusion
• Synthetic control estimates counterfactual outcomes

Innovation

• Community Notes fact-checking feature
• Synthetic control methods analysis
• Reduced false content engagement