关键词: 谣言检测, 多领域, 多模态, 领域专家, 图文歧义

Abstract: Multimodal online rumors combining images and texts have become a prominent issue disrupting social order and affecting social stability due to their high deceptiveness and rapid spread. However, content-based detection methods often suffer from "negative transfer" and "seesaw" effects when processing multi-domain data, resulting in significant performance fluctuations across different vertical domains despite an increase in overall accuracy. To address this, an online Rumors Detection model fusing Hierarchical domain Experts and image-text Ambiguity Discrimination (RDHEAD） was proposed to enhance cross-domain adaptability while ensuring detection accuracy. First, an expert feature decoupling framework was designed to separate domain-general and domain-specific features by employing parallel sharing and unique expert networks combined with adversarial training and domain supervision. Second, an image-text ambiguity discrimination module based on cross-attention enhancement and Chinese CLIP fine-tuning was constructed to capture cross-modal conflicts. Finally, a two-stage gating network was utilized to dynamically fuse various features for rumor detection. Experimental results on the Weibo21 dataset showed that the macro F1 score of RDHEAD reached 92.8%, which is 0.5 percentage points higher than that of the CFPFND (multi-domain fake News Detection based on Cross-Feature Perception Fusion) model. Meanwhile, on the F1 Standard Deviation (F1-STD) metric evaluating inter-domain balance, RDHEAD is only 0.016, a 33% reduction compared to the state-of-the-art CCFMND (Cross-Correlation Feature and Memory-based Network for Detection) model. The experimental results verified that the proposed model effectively mitigated the "negative transfer" and "seesaw" problems in cross-domain rumor detection while improving detection accuracy.

Key words: rumor detection, multi-domain, multi-modal, domain experts, image-text ambiguity