MRAFnd: Multimodal Retrieval-Augmented Framework for Zero-Shot Fake News Detection

2026-08-02

AI Summary

This work addresses the challenge of multimodal fake news detection for emerging events in zero-shot scenarios, where existing methods often overlook the reuse patterns of historical disinformation and subtle cross-modal inconsistencies. To tackle this, we propose the first framework that integrates retrieval augmentation with multi-agent collaborative debate: it first retrieves relevant historical news articles via multimodal similarity search, then leverages bidirectional evidence reasoning within a structured debate mechanism to assess veracity. By effectively capturing cross-modal discrepancies and characteristic patterns of past deceptive strategies, our approach significantly outperforms state-of-the-art methods across three benchmark datasets, achieving an accuracy improvement of up to 2.35% on Weibo-21.

Abstract

The rapid dissemination of multimodal content has intensified the spread of fabricated news, presenting a substantial threat to social integrity. A formidable challenge for current detection systems is identifying misinformation related to novel events in zero-shot scenarios. Prevailing zero-shot methods typically assess news items in isolation via semantic matching, a strategy that fails to recognize the recycled disinformation tactics from past campaigns and lacks the sophisticated reasoning needed to identify subtle, cross-modal discrepancies. To surmount these deficiencies, we introduce \textbf{MRAFnd}, a novel \underline{\textbf{M}}ultimodal \underline{\textbf{R}}etrieval-\underline{\textbf{A}}ugmented Framework for Zero-Shot \underline{\textbf{F}}ake \underline{\textbf{N}}ews \underline{\textbf{D}}etection. MRAFnd emulates a collaborative team of analysts to verify news veracity. The framework initiates with \textbf{Multimodal Similarity-based News Retrieval} to assemble a corpus of contextually analogous articles from an unlabeled reference database. Subsequently, during the \textbf{Bifurcated Evidential Reasoning} stage, agents perform a dual-directional analysis to extract critical patterns from the retrieved evidence. Finally, a \textbf{Multi-Agent Collaborative Debate}, involving Analyst and Arbiter agents, engages in a structured discourse to arrive at a definitive and robust conclusion. Comprehensive experiments on three benchmark datasets reveal that MRAFnd markedly surpasses state-of-the-art baselines, achieving an accuracy gain of up to 2.35\% on the demanding Weibo-21 dataset.

Problem

• zero-shot fake news detection
• multimodal misinformation
• cross-modal discrepancy
• novel event verification
• recycled disinformation tactics

Innovation

• Multimodal Retrieval
• Zero-Shot Fake News Detection
• Evidential Reasoning
• Multi-Agent Debate
• Cross-Modal Discrepancy