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

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 MRAFnd, a novel \underline{M}ultimodal \underline{R}etrieval-\underline{A}ugmented Framework for Zero-Shot \underline{F}ake \underline{N}ews \underline{D}etection. MRAFnd emulates a collaborative team of analysts to verify news veracity. The framework initiates with Multimodal Similarity-based News Retrieval to assemble a corpus of contextually analogous articles from an unlabeled reference database. Subsequently, during the Bifurcated Evidential Reasoning stage, agents perform a dual-directional analysis to extract critical patterns from the retrieved evidence. Finally, a 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.