Semantic Reshuffling with LLM and Heterogeneous Graph Auto-Encoder for Enhanced Rumor Detection

Abstract

Social media is crucial for information spread, necessitating effective rumor detection to curb misinformation’s societal effects. Current methods struggle against complex propagation influenced by bots, coordinated accounts, and echo chambers, which fragment information and increase risks of misjudgments and model vulnerability. To counteract these issues, we introduce a new rumor detection framework, the Narrative-Integrated Metapath Graph Auto-Encoder (NIMGA). This model consists of two core components: (1) Metapath-based Heterogeneous Graph Reconstruction. (2) Narrative Reordering and Perspective Fusion. The first component dynamically reconstructs propagation structures to capture complex interactions and hidden pathways within social networks, enhancing accuracy and robustness. The second implements a dual-agent mechanism for viewpoint distillation and comment narrative reordering, using LLMs to refine diverse perspectives and semantic evolution, revealing patterns of information propagation and latent semantic correlations among comments. Extensive testing confirms our model outperforms existing methods, demonstrating its effectiveness and robustness in enhancing rumor representation through graph reconstruction and narrative reordering.