Rumor detection technology based on ubiquitous relationship

Abstract

This paper addresses the limitations of existing rumor detection methods that heavily rely on single or local features, which restrict their ability to capture comprehensive and detailed characteristics of rumors. The main objective of this study is to enhance the efficiency of rumor detection. To achieve this, we propose a novel approach that integrates user attributes, comment structure, and propagation models, introducing the concept of ubiquitous relationships for messages in social networks. We construct a Tweet-word-user ubiquitous relationship network using a propagation model and further leverage the Graph Convolutional Neural Network (GCN) to enhance semantic features. Consequently, we present a novel rumor detection model, the Ubiquitous Relationship-based Graph Convolutional Neural Network (U-GCN), which effectively combines user, text, and comment features within a unified framework, while also enhancing semantic features from the source post for comprehensive detection. Extensive experiments are conducted on two publicly available Twitter Datasets. The results demonstrate that our proposed U-GCN model achieves an accuracy rate of above 0.9, outperforming methods that solely consider single or local features. Our findings highlight the effectiveness of leveraging ubiquitous relationships in rumor detection.