LCFND: OT–guided LLM evidence reasoning and graph verification for multimodal fake news detection
Multimodal fake news is difficult to detect when text and image remain topically aligned but diverge in local factual details such as entities, locations, time, or event attributes. Existing methods often rely on global cross-modal fusion and may therefore overlook these localized contradictions. We present LCFND, a staged and fully specified bounded-evidence verification pipeline for localized conflict discovery, structured evidence generation, and graph-based verification. Optimal transport identifies high-conflict token-region pairs and constructs localized evidence packets. A parameter-efficient LLM trained with OT-grounded structured targets and training-split label-informed weak relation calibration then converts these packets into structured evidence, including claims, visual evidence, contradiction types, and grounded rationales. A heterogeneous evidence graph integrates OT conflict priors and LLM-generated evidence for veracity prediction, and all graph-construction rules, training objectives, and evaluation protocols are stated explicitly for reproducibility. Under a controlled matched-backbone protocol on Weibo, Twitter, and GossipCop, LCFND improves F1 over the top matched-backbone baseline MGCA by 1.47, 1.73, and 1.67 points, respectively. The gain remains positive when the visual backbone is strengthened to ViT-B/16 or Swin-T. Manual evidence evaluation on 500 samples per dataset reports 85.9–87.3% claim grounding and 78.1–80.2% contradiction-type correctness. Under the P3b matched-input protocol, LCFND remains above FND-LLM-M and MMRGV-lite, and improves average cross-dataset transfer F1 from 67.10 to 69.98. Full online inference takes 1.47 s per sample in our setting, so the framework is better suited to offline or high-risk verification than to large-scale real-time screening.