Abstract
Social media has become an ideal platform for the propagation of rumors, fake news, and misinformation. Rumors on social media not only mislead online users but also affect the real world immensely. Thus, detecting the rumors and preventing their spread became an essential task. Some of the recent deep learning-based rumor detection methods, such as Bi-Directional Graph Convolutional Networks (Bi-GCN), represent rumor using the completed stage of the rumor diffusion and try to learn the structural information from it. However, these methods are limited to represent rumor propagation as a static graph, which isn’t optimal for capturing the dynamic information of the rumors. In this study, we propose novel graph convolutional networks with attention mechanisms, named Dynamic GCN, for rumor detection. We first represent rumor posts with their responsive posts as dynamic graphs. The temporal information is used to generate a sequence of graph snapshots. The representation learning on graph snapshots with attention mechanism captures both structural and temporal information of rumor spreads. The conducted experiments on three real-world datasets demonstrate the superiority of Dynamic GCN over the state-of-the-art methods in the rumor detection task.
Highlights
Social media has been a great disseminator for new information and thoughts
The reported performances are accuracies and F1 scores of Dynamic GCN (DYNGCN) with both additive attention (ADD) and dot-product attention (DOT) with the sequential (S) snapshots or temporal (T) snapshots size of 3
It is demonstrated that the traditional machine learning-based methods with handcrafted features, (DTC, RFC, SVM-TS, SVM-TK), show lower performances compare to other deep learning-based methods (GRU, RvNN, BiGCN, DYNGCN)
Summary
Social media has been a great disseminator for new information and thoughts. Due to its accessibility of sharing information, social media has become an ideal platform for propagations of rumors, fake news, and misinformation [1]. The proposed rumor detection models were able to capture the high-dimensional representation from the textural contents, user profiles, and propagation structures. [26, 27] successfully adopted GCN and GAT in the rumor detection domain, respectively Both models aren’t considering the temporal dynamics of the rumor propagation, which only considers the static graph structure of the final state of rumor propagation. We propose a novel GCN-based rumor detection model that can capture the evolving pattern of rumor propagation by aggregating the structural representations of snapshot sequences. The manually extracted content-based, userbased, temporal, or propagation-based handcrafted features were used to train classical machine learning classifiers such as a decision tree, random forest, or SVMs. the limitation of models with handcrafted features is that they fail to capture the high-dimensional patterns of rumors. Sophisticated models like GCN [26] or GAT [27] have successfully been adopted in the rumor detection domain
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