ExDR: Explanation-driven Dynamic Retrieval Enhancement for Multimodal Fake News Detection
This addresses the societal problem of rapidly spreading multimodal fake news, though it appears to be an incremental improvement over existing dynamic retrieval methods.
The paper tackles multimodal fake news detection by proposing ExDR, an explanation-driven dynamic retrieval framework that uses model-generated explanations to improve retrieval triggering and evidence selection. Experiments on AMG and MR2 datasets show ExDR outperforms previous methods in retrieval accuracy and detection performance.
The rapid spread of multimodal fake news poses a serious societal threat, as its evolving nature and reliance on timely factual details challenge existing detection methods. Dynamic Retrieval-Augmented Generation provides a promising solution by triggering keyword-based retrieval and incorporating external knowledge, thus enabling both efficient and accurate evidence selection. However, it still faces challenges in addressing issues such as redundant retrieval, coarse similarity, and irrelevant evidence when applied to deceptive content. In this paper, we propose ExDR, an Explanation-driven Dynamic Retrieval-Augmented Generation framework for Multimodal Fake News Detection. Our framework systematically leverages model-generated explanations in both the retrieval triggering and evidence retrieval modules. It assesses triggering confidence from three complementary dimensions, constructs entity-aware indices by fusing deceptive entities, and retrieves contrastive evidence based on deception-specific features to challenge the initial claim and enhance the final prediction. Experiments on two benchmark datasets, AMG and MR2, demonstrate that ExDR consistently outperforms previous methods in retrieval triggering accuracy, retrieval quality, and overall detection performance, highlighting its effectiveness and generalization capability.