Structure-adaptive Adversarial Contrastive Learning for Multi-Domain Fake News Detection

The rapid proliferation of fake news across multiple domains poses significant threats to society.Existing multi-domain detection models typically capture domain-shared semantic features to achieve generalized detection.However, they often fail to generalize well due to poor adaptability, which limits their ability to provide complementary features for detection, especially in data-constrained conditions.To address these challenges, we investigate the propagation-adaptive multi-domain fake news detection paradigm.We propose a novel framework, Structure-adaptive Adversarial Contrastive Learning (StruACL), to adaptively enable structure knowledge transfer between multiple domains.Specifically, we first contrast representations between content-only and propagation-rich data to preserve structural patterns in the shared representation space.Additionally, we design a propagation-guided adversarial training strategy to enhance the diversity of representations.Under the StruACL objective, we leverage a unified Transformerbased and graph-based model to jointly learn transferable semantic and structural features for detection across multiple domains.Experiments on seven fake news datasets demonstrate that StruACL-TGN achieves better multidomain detection performance on general and data-constrained scenarios, showing the effectiveness and better generalization of StruACL.