GLED-Net: A Global-Local Evidence Decomposition Network for Multimodal Fake News Detection

Multimodal fake news detection requires reasoning about both the overall consistency of an image–text pair and a compact set of textual cues that may carry discriminative information. We propose GLED-Net, a CLIP-based global–local evidence decomposition framework that organizes these views into a global narrative stream and a local cue stream. The global stream models the correspondence between the complete post and its image, whereas the local stream uses automatically extracted keywords to construct a complementary representation. Branch-level supervision encourages both streams to remain predictive, and a learned gate combines their outputs. Experiments on Weibo-16 and FNDC show strong performance relative to the evaluated supervised and zero-shot baselines. Same-backbone controls, component ablations, sensitivity analysis, and repeated runs distinguish the effect of the pretrained encoder from that of the proposed evidence organization. The results also show that the benefit of heuristic keyword cues and adaptive fusion is dataset-dependent, which motivates more adaptive and verifiable cue selection.