ExDR: Explanation-driven Dynamic Retrieval Enhancement for Multimodal Fake News Detection

TL;DR

ExDR introduces an explanation-driven retrieval framework that enhances multimodal fake news detection by leveraging model explanations and deception-specific evidence, leading to improved accuracy and robustness.

Contribution

It presents a novel framework that systematically uses explanations for better retrieval and detection of fake news, addressing limitations of previous retrieval methods.

Findings

Outperforms previous methods in retrieval triggering accuracy

Achieves higher detection performance on benchmark datasets

Demonstrates strong generalization across datasets

Abstract

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.