A unified multimodal large model framework for cross-platform fake news spread chain detection

We introduce a unified multimodal framework for cross-platform fake news spread-chain detection. The approach jointly models multimodal content and multi-platform propagation to trace misinformation flows across social ecosystems. It integrates multimodal large models for semantic encoding under content transformations, cross-platform alignment to link related posts, typed graph construction combining in-platform and cross-platform relations, and chain-level classification via relation-aware graph reasoning. Evaluated on six benchmarks (FakeNewsNet, Fakeddit, PHEME, Twitter15/16, MCFEND) under a leave-one-platform-out protocol, our method outperforms state-of-the-art baselines by +3.5% Macro-F1, achieves robust chain detection (edge F1: 0.83, purity: 0.84), and demonstrates strong generalisation across platforms, reducing performance drop from 15% to 8%. Ablation studies validate each component’s contribution. The framework provides explainable, chain-level evidence that supports downstream moderation and forensic analysis. This work advances misinformation detection beyond single-platform, post-level analysis toward integrated, traceable, and platform-agnostic monitoring.