SMD-Net: Selective and Multiway Differential Perception-Enhanced Network for Early Multimodal Rumor Detection

Full abstract

Read the full abstract

The rapid dissemination of rumors on social media at their early stages poses significant threats to public safety and social stability. While response-based methods usually depend on user comments and reposts and therefore suffer from inherent latency, existing content-based methods still struggle to extract discriminative evidence from noisy short texts and subtle visual inconsistencies under zero-response conditions. To address this issue, we propose SMD-Net, a multimodal framework for early zero-response rumor detection. In the textual branch, a selective state-space encoder is used to model fragmented and noisy posts. In the visual branch, an enhanced TransXNet backbone is designed to improve the representation of fine-grained suspicious patterns and cross-layer feature interactions. An adaptive gated fusion module is further introduced to integrate textual and visual features for final prediction. Experiments on the Weibo and PHEME datasets show that SMD-Net outperforms the compared content-based baselines, achieving 92.60% accuracy on Weibo and 90.27% accuracy on PHEME under the strict zero-response setting. These results suggest that the proposed framework provides an effective solution for early multimodal rumor detection when propagation-based evidence is unavailable.