Emotion-aware Structural Enhancement Graph Auto-Encoder for Rumor Detection

Abstract

Social media is a key channel for information dissemination, making effective rumor detection essential to mitigate misinformation’s societal impact. Although large language models excel in inference and text generation, they struggle with understanding propagation relationships and complex reasoning tasks like rumor detection. Existing methods mainly rely on textual information and event propagation structures, but provocative comments and unreliable interactions increase propagation uncertainty. To address these challenges, we propose an Emotionally-Aware Structural Enhancement Graph Auto-Encoder (EASE-GARD) to improve rumor representations. Our method begins by enhancing the textual representation of responses through the generation of emotive adversarial comments. It then generates and differentiates false local propagation relationships (fabricated forwards and reciprocations) to reduce propagation uncertainties. A graph auto-encoder captures contextual features and global structural information and recalculates forwarding probabilities among responses. Extensive experiments show our model performs best on all three datasets, excelling in effectiveness and robustness.