Integrating Linguistic Semantics and Sentiment for Multimodal Rumor Detection

With the rapid development of social media, the speed and influence of rumor dissemination continue to increase, posing severe challenges to information environment governance. Existing rumor detection methods still face limitations in multimodal alignment, and emotion modeling, making them insufficient for the Weibo scenario characterized by short texts, heterogeneous modalities, and complex propagation patterns. This paper proposes a multimodal rumor detection framework tailored for Weibo, which jointly models text, image, and social features. Specifically, semantic and emotional sub-channels are designed for both text and image modalities, while social statistical features are introduced as a third modality, resulting in a three-modality, five-branch architecture. In the fusion stage, a gating mechanism combined with modality-level dropout is designed to provide more stable fusion under heterogeneous modalities. Finally, a lightweight feed-forward classifier performs the final prediction. Experimental results on the Weibo dataset demonstrate that the proposed method significantly outperforms mainstream approaches, achieving overall Accuracy = 0.883 and Macro-F1 = 0.883, compared with TRANSFAKE (Accuracy = 0.855) and MPFN (Accuracy = 0.838). In terms of class-specific performance, the model attains the best results on non-rumor detection with Recall = 0.926 and F1 = 0.888, while maintaining the highest Precision = 0.918 for rumor classification, showing a more balanced discriminative ability. Further ablation studies confirm the effectiveness of the proposed fusion mechanism in enhancing model stability and interpretability. The overall framework provides an efficient multimodal solution for rumor detection in social media contexts.