Explainable rumor detection based on grey clustering: Fusion of manual features and deep learning features

The importance of rumor detection on social media is self-evident. However, many existing studies have focused on exploring potential features in text content and propagation patterns, while neglecting a key aspect-the explainability of the model. The comment content can provide support for the credibility of the detection. Nevertheless, most studies that use comments encode them into specific models, rarely considering their semantic attitudes and standpoints, making it difficult for models to explain why a post is a rumor. In this study, we propose an Explainable rumor detection model based on Grey clustering called MDE-Grey, which combines Manual features and Deep learning features. In terms of manual features, we constructed a relevant vocabulary based on the specific comment environment of rumors to capture comment standpoints. In terms of deep learning features, we have designed a GCN sub network that includes two attention mechanisms to capture noteworthy content in posts and comments. Finally, we constructed a new grey clustering model to fuse the two types of features and obtain the final prediction. In the grey clustering model, we designed new whitening functions to capture the intrinsic relationship between features and rumor categories, ensuring the traceability of prediction results. The experiments on three datasets and case studies have demonstrated the effectiveness of the MDE-Grey model in detecting rumors and explaining the results.