# RAVE: Retrieval and Scoring Aware Verifiable Claim Detection

arXiv: https://arxiv.org/abs/2509.15793 · HTML: https://mlsanity.com/paper/2509.15793/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection](https://mlsanity.com/paper/1809.08193/) (2018)
- [Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Research](https://mlsanity.com/paper/2401.11969/) (2024)
- [AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators](https://mlsanity.com/paper/2402.11073/) (2024)
- [ClaimRank: Detecting Check-Worthy Claims in Arabic and English](https://mlsanity.com/paper/1804.07587/) (2018)
- [Accenture at CheckThat! 2020: If you say so: Post-hoc fact-checking of claims using transformer-based models](https://mlsanity.com/paper/2009.02431/) (2020)
- [FactFinders at CheckThat! 2024: Refining Check-worthy Statement Detection with LLMs through Data Pruning](https://mlsanity.com/paper/2406.18297/) (2024)
- [Information Credibility in the Social Web: Contexts, Approaches, and Open Issues](https://mlsanity.com/paper/2001.09473/) (2020)

## Built on by (most cited first, 1 of 1)

- [ContextClaim: A Context-Driven Paradigm for Verifiable Claim Detection](https://mlsanity.com/paper/2603.30025/) (2026)

## Abstract (arXiv)

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.