Avoiding Over-smoothing in Social Media Rumor Detection with Pre-trained Propagation Tree Transformer
Deep learning techniques for rumor detection typically utilize Graph Neural Networks (GNNs) to analyze post relations. These methods, however, falter due to over-smoothing issues when processing rumor propagation structures, leading to declining performance. Our investigation into this issue reveals that over-smoothing is intrinsically tied to the structural characteristics of rumor propagation trees, in which the majority of nodes are 1-level nodes. Furthermore, GNNs struggle to capture long-range dependencies within these trees. To circumvent these challenges, we propose a Pre-Trained Propagation Tree Transformer (P2T3) method based on pure Transformer architecture. It extracts all conv...
| Authors | Chaoqun Cui, Caiyan Jia |
|---|---|
| Primary category | cs.CL |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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