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.

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