RAVE: Retrieval and Scoring Aware Verifiable Claim Detection
This addresses the need for scalable fact-checking tools to combat misinformation on social media, though it appears incremental as it builds on prior claim detection approaches.
The paper tackles the problem of detecting verifiable claims in social media misinformation by proposing RAVE, a framework that combines evidence retrieval with structured signals of relevance and source credibility, achieving consistent outperformance over baselines in accuracy and F1 on CT22-test and PoliClaim-test datasets.
The rapid spread of misinformation on social media underscores the need for scalable fact-checking tools. A key step is claim detection, which identifies statements that can be objectively verified. Prior approaches often rely on linguistic cues or claim check-worthiness, but these struggle with vague political discourse and diverse formats such as tweets. We present RAVE (Retrieval and Scoring Aware Verifiable Claim Detection), a framework that combines evidence retrieval with structured signals of relevance and source credibility. Experiments on CT22-test and PoliClaim-test show that RAVE consistently outperforms text-only and retrieval-based baselines in both accuracy and F1.