关键词: 谣言检测, 情感分析, 多模态融合, 注意力机制, 群体性事件

Abstract: Rumors involving public safety, disasters, and other mass incidents often contain rich emotional features in text or images, which easily mobilize netizens’ emotional responses, inducing them to like, comment, and share. However, existing multimodal rumor detection methods lack effective extraction techniques for the emotional features contained in multimodal data and fail to consider the interrelationship between modalities during feature fusion, resulting in redundant and less accurate feature representations. To explore the role of cross-modal emotional features in rumor detection, a multimodal rumor detection method that integrates multi-granularity emotional features of image-text is proposed. Without relying on social information such as comments and dissemination patterns, this method integrates multi-granularity emotional features into the multimodal rumor detection process. It employs a cross-modal multi-granularity emotional feature fusion method based on an interactive attention mechanism to fully integrate deep features of multimedia information. To evaluate the effectiveness of the proposed method, comparative and ablation experiments are conducted on two public datasets, Weibo and Twitter. The results indicate that the proposed method improves rumor detection accuracy to 0.912 on the Weibo dataset and 0.839 on the Twitter dataset, showing superior performance across multiple metrics such as F1 value, effectively enhancing rumor detection performance and the interpretability of the model. To some extent, it can assist public security agencies in handling rumors during mass incidents, providing technical support for grassroots police operations.

Key words: rumor detection, emotional analysis, multimodal fusion, attention mechanism, mass incidents