Contrastive Prompt Learning in Structured Graph Networks for Multimodal Fake News Detection

Abstract

With social media now the primary channel for information dissemination, multimodal disinformation poses a severe challenge to cyberspace governance and public safety due to its heightened deceptiveness and viral potential. Current detection methods predominantly rely on simple feature concatenation or shallow interaction models, which struggle to capture semantic relationships and inconsistencies across modalities. Moreover, they also universally face the semantic gap issue caused by misalignment between pre-training tasks and downstream classification objectives. To address these issues, this paper proposes a Contrastive Prompt Learning in Structured Graph Networks (CPLS) model. CPLS comprises three core modules: 1) Structured graph network construction module that builds graph structures preserving key semantics through semantic-aware graph pruning and fusion strategies; 2) Structured graph network optimization module that uses data augmentation and contrastive learning to capture robust local node features and global graph structures; 3) Contrastive prompt learning module that innovatively integrates prompt learning with graph contrastive learning, reframing downstream classification tasks as contrastive tasks to unify optimization of pre-training and downstream objectives. Experiments on public datasets show that our method achieves accuracy improvements of 2.4% on Weibo and 4.5% on Twitter.