SIFTNet: Structure-Guided Iterative Fusion with a Transformer Network for Fake News Detection

Fake news detection has become critical for safeguarding social media users and maintaining a reliable news ecosystem. However, existing methods rely mainly on context information and propagation structure and do not consider the news structure from framing theory. As a highly structured genre, news implies writing intention and organizational logic in its discourse frame, which provides vital clues for authenticity verification. In this paper, we propose structure-guided iterative fusion with a transformer network for fake news detection (SIFTNet), which contains four modules: a structural label generator, an information architecture representation module, a structure-enhanced representation module, and a structure-guided iterative fusion module. Guided by framing theory, SIFTNet captures the semantics at both the local sentence level and global structure level. Extensive experiments demonstrate that our model achieves state-of-the-art performance on both Chinese and English datasets, exhibiting superior effectiveness and robustness. These findings validate the efficacy of applying framing theory to improve fake information detection.