Modality Perception Learning-Based Determinative Factor Discovery for Multimodal Fake News Detection

The dissemination of fake news, often fueled by exaggeration, distortion, or misleading statements, significantly jeopardizes public safety and shapes social opinion. Although existing multimodal fake news detection methods focus on multimodal consistency, they occasionally neglect modal heterogeneity, missing the opportunity to unearth the most related determinative information concealed within fake news articles. To address this limitation and extract more decisive information, this article proposes the modality perception learning-based determinative factor discovery (MoPeD) model. MoPeD optimizes the steps of feature extraction, fusion, and aggregation to adaptively discover determinants within both unimodality features and multimodality fusion features for the task of fake news detection. Specifically, to capture comprehensive information, the dual encoding module integrates a modal-consistent contrastive language-image pre-training (CLIP) pretrained encoder with a modal-specific encoder, catering to both explicit and implicit information. Motivated by the prompt strategy, the output features of the dual encoding module are complemented by learnable memory information. To handle modality heterogeneity during fusion, the multilevel cross-modality fusion module is introduced to deeply comprehend the complex implicit meaning within text and image. Finally, for aggregating unimodal and multimodal features, the modality perception learning module gauges the similarity between modalities to dynamically emphasize decisive modality features based on the cross-modal content heterogeneity scores. The experimental evaluations conducted on three public fake news datasets show that the proposed model is superior to other state-of-the-art fake news detection methods.