# Fact-Checking with Contextual Narratives: Leveraging Retrieval-Augmented LLMs for Social Media Analysis

arXiv: https://arxiv.org/abs/2504.10166 · HTML: https://mlsanity.com/paper/2504.10166/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2025.

## Built on by (most cited first, 2 of 2)

- [SNS-Bench-VL: Benchmarking Multimodal Large Language Models in Social Networking Services](https://mlsanity.com/paper/2505.23065/) (2025)
- [Fact or Fake? Assessing the Role of Deepfake Detectors in Multimodal Misinformation Detection](https://mlsanity.com/paper/2602.01854/) (2026)

## Abstract (arXiv)

We propose CRAVE (Cluster-based Retrieval Augmented Verification with Explanation); a novel framework that integrates retrieval-augmented Large Language Models (LLMs) with clustering techniques to address fact-checking challenges on social media. CRAVE automatically retrieves multimodal evidence from diverse, often contradictory, sources. Evidence is clustered into coherent narratives, and evaluated via an LLM-based judge to deliver fact-checking verdicts explained by evidence summaries. By synthesizing evidence from both text and image modalities and incorporating agent-based refinement, CRAVE ensures consistency and diversity in evidence representation. Comprehensive experiments demonstrate CRAVE's efficacy in retrieval precision, clustering quality, and judgment accuracy, showcasing its potential as a robust decision-support tool for fact-checkers.