MWDLS:考虑语言风格特征的多模态在线健康谣言检测模型

[Objective] To address the insufficient attention in existing literature to the language style characteristics of rumors and the partially truthful dual-faced health information, this paper proposes a multimodal online health rumor detection model incorporating language style features (MWDLS: A Multimodal Wide and Deep Model for Online Health Rumor Detection Considering Language Style). [Methods] The MWDLS model leverages Aristotle’s rhetorical theory to extract persuasive language style features— appealing to emotion, logic, and character—and employs a bidirectional cross-modal interaction fusion strategy with a gating mechanism to achieve joint representation learning and classification prediction of shallow language style features and deep semantic features. [Results] We conducted extensive experiments on a real-world dataset from a leading Chinese social media platform and found that MWDLS outperformed the baseline models. It improved the F1 score of the target task by up to 11.98 percentage points. Notably, for the health rumor category and the dual-faced health information category, MWDLS increased the F1 scores by up to 16.63 and 11.71 percentage points, respectively. [Limitations] The current model does not examine other modalities, such as video and audio, nor does it incorporate large language models or knowledge-aware mechanisms to enhance early detection of health rumors. [Conclusions] By integrating language style features with multimodal deep semantic features, MWDLS effectively enhances the performance of online health rumor detection.