RASR: Retrieval-Augmented Semantic Reasoning for Fake News Video Detection

• URL: http://arxiv.org/abs/2604.06687v1
• Date: Wed, 08 Apr 2026 05:03:34 GMT
• Title: RASR: Retrieval-Augmented Semantic Reasoning for Fake News Video Detection
• Abstract summary: Multimodal fake news video detection is a crucial research direction for maintaining the credibility of online information.<n>Existing studies primarily verify content authenticity by constructing multimodal feature fusion representations or utilizing pre-trained language models.<n>We propose a novel Retrieval-Augmented Semantic Reasoning (RASR) framework to overcome these limitations.
• Score: 38.069086560563285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal fake news video detection is a crucial research direction for maintaining the credibility of online information. Existing studies primarily verify content authenticity by constructing multimodal feature fusion representations or utilizing pre-trained language models to analyze video-text consistency. However, these methods still face the following limitations: (1) lacking cross-instance global semantic correlations, making it difficult to effectively utilize historical associative evidence to verify the current video; (2) semantic discrepancies across domains hinder the transfer of general knowledge, lacking the guidance of domain-specific expert knowledge. To this end, we propose a novel Retrieval-Augmented Semantic Reasoning (RASR) framework. First, a Cross-instance Semantic Parser and Retriever (CSPR) deconstructs the video into high-level semantic primitives and retrieves relevant associative evidence from a dynamic memory bank. Subsequently, a Domain-Guided Multimodal Reasoning (DGMP) module incorporates domain priors to drive an expert multimodal large language model in generating domain-aware, in-depth analysis reports. Finally, a Multi-View Feature Decoupling and Fusion (MVDFF) module integrates multi-dimensional features through an adaptive gating mechanism to achieve robust authenticity determination. Extensive experiments on the FakeSV and FakeTT datasets demonstrate that RASR significantly outperforms state-of-the-art baselines, achieves superior cross-domain generalization, and improves the overall detection accuracy by up to 0.93%.

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