Rumor Detection in Social Media Based on Multi-Hop Graphs and Differential Time Series

The widespread dissemination of rumors (fake information) on online social media has had a detrimental impact on public opinion and the social environment. This necessitates the urgent need for efficient rumor detection methods. In recent years, deep learning techniques, including graph neural networks (GNNs) and recurrent neural networks (RNNs), have been employed to capture the spatiotemporal features of rumors. However, existing research has largely overlooked the limitations of traditional GNNs based on message-passing frameworks when dealing with rumor propagation graphs. In fact, due to the issues of excessive smoothing and gradient vanishing, traditional GNNs struggle to capture the interactive information among high-order neighbors when handling deep graphs, such as those in rumor propagation scenarios. Furthermore, previous methods used for learning the temporal features of rumors, whether based on dynamic graphs or time series, have overlooked the importance of differential temporal information. To address the aforementioned issues, this paper proposes a rumor detection model based on multi-hop graphs and differential time series. Specifically, this model consists of two components: the structural feature extraction module and the temporal feature extraction module. The former utilizes a multi-hop graph and the enhanced message passing framework to learn the high-order structural features of rumor propagation graphs. The latter explicitly models the differential time series to learn the temporal features of rumors. Extensive experiments conducted on multiple real-world datasets demonstrate that our proposed model outperforms the previous state-of-the-art methods.