COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations

TL;DR

This paper introduces COMMUNITYNOTES, a large multilingual dataset for evaluating the helpfulness of fact-checking explanations, and proposes a framework to predict helpfulness and reasons, improving fact-checking systems.

Contribution

The paper presents a new dataset and a novel framework for predicting the helpfulness and reasons of fact-checking explanations, enhancing community-based fact-checking.

Findings

Optimized reason definitions improve prediction accuracy.

Helpful explanations enhance fact-checking system performance.

Large-scale multilingual dataset enables diverse analysis.

Abstract

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 In practice, most community notes remain unpublished due to slow community annotation, and the reasons for helpfulness lack clear definitions.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 is beneficial for existing fact-checking systems.