COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations
This addresses the challenge of slow annotation and unclear helpfulness criteria in community fact-checking systems, with incremental improvements to existing methods.
The paper tackles the problem of predicting the helpfulness of community-based fact-checking explanations and the reasons for their helpfulness, introducing a dataset of 104k posts and showing that optimized definitions improve prediction performance.
Fact-checking on major platforms, such as X, Meta, and TikTok, is shifting from expert-driven verification to a community-based setup, where users contribute explanatory notes to clarify why a post might be misleading. An important challenge here is determining whether an explanation is helpful for understanding real-world claims and the reasons why, which remains largely underexplored in prior research. In practice, most community notes remain unpublished due to slow community annotation, and the reasons for helpfulness lack clear definitions. To bridge these gaps, we introduce the task of predicting both the helpfulness of explanatory notes and the reason for this. We present COMMUNITYNOTES, a large-scale multilingual dataset of 104k posts with user-provided notes and helpfulness labels. We further propose a framework that automatically generates and improves reason definitions via automatic prompt optimization, and integrate them into prediction. Our experiments show that the optimized definitions can improve both helpfulness and reason prediction. Finally, we show that the helpfulness information are beneficial for existing fact-checking systems.