Dynamic graph convolutional networks with attention mechanism for rumor detection on social media

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 d...

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Bibliographic Details
Published in:PloS one 2021-08, Vol.16 (8), p.e0256039-e0256039
Main Authors: Choi, Jiho, Ko, Taewook, Choi, Younhyuk, Byun, Hyungho, Kim, Chong-Kwon
Format: Article
Language:English
Subjects:
Artificial neural networks
Biology and Life Sciences
Causes of
Communication
Computer and Information Sciences
Computer science
Datasets
Deep learning
Digital media
Disinformation
Engineering
Evaluation
Experiments
False information
Gossip
Graphical representations
Human Activities - psychology
Humans
Information Dissemination - methods
Information management
Machine learning
Methods
Models, Theoretical
Neural networks
Neural Networks, Computer
Propagation
Recognition, Psychology - physiology
Social media
Social Media - standards
Social networks
Social Sciences
Software
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0256039