Abstract: Fake news often uses images that superficially match the textual content but contradict it in deeper meaning,making the news appear plausible and misleading the audience.Therefore,multimodal fake news detection is confronted with the challenge of the “image-text rationalization” phenomenon.This paper proposes a “image-text rationalization” fake news detection based on hypergraph.The cross-modal interaction enhancement module generates guiding weights for bidirectional feature enhancement to establish semantic alignment of unimodal features.The hypergraph learning module concatenates unimodal features to form multimodal news representations,introduces a connection strength measure between news nodes and hyperedges,and applies an adaptive hyperedge number adjustment strategy to construct a hypergraph that captures complex high-order relations among news.The multimodal classification module aggregates unimodal and high-order relational representations for detection.Experimental results show that the proposed method achieves state-of-the-art performance,with accuracy gains of 1.6 percentage points on the Chinese Weibo dataset and 3.3 percentage points on the English Twitter dataset,and with F1 score gains of 1.6 percentage points and 3.8 percentage points,respectively.

Key words: Multimodal fake news detection, Hypergraph learning, Image-text rationalization, Higher-order relationship modeling