RAVE: Retrieval and Scoring Aware Verifiable Claim Detection

2025-09-19

AI Summary

The proliferation of misinformation on social media necessitates scalable fact-checking technologies, for which claim detection—identifying objectively verifiable statements—is a critical prerequisite. Existing approaches rely primarily on linguistic cues or claim-worthiness estimation, exhibiting limited robustness when handling ambiguous political statements and short, heterogeneous texts (e.g., tweets). To address semantic ambiguity and format heterogeneity, we propose a retrieval-augmented claim detection framework that jointly models evidence relevance structure signals and source credibility. Our method integrates external knowledge via retrieval while explicitly encoding structural relationships among candidate claims and supporting evidence, alongside calibrated source reliability scores. Evaluated on CT22-test and PoliClaim-test benchmarks, our approach significantly outperforms pure-text baselines and conventional retrieval-based methods, achieving state-of-the-art accuracy and F1-score. Results demonstrate that synergistic multi-signal modeling substantially enhances claim detection performance in complex, real-world contexts.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Detecting verifiable claims in social media content
• Overcoming vague political discourse and diverse formats
• Combining evidence retrieval with relevance and credibility signals

Innovation

• Combines evidence retrieval with relevance signals
• Integrates source credibility scoring mechanisms
• Outperforms text-only and retrieval baselines