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Mixed Graph Neural Network-Based Fake News Detection for Sustainable Vehicular Social Networks
journal contribution
posted on 2022-09-29, 06:08 authored by Z Guo, K Yu, A Jolfaei, Gang LiGang Li, F Ding, A BeheshtiThe rapid development of the Internet of Vehicles has substantially boosted the prevalence of vehicular social networks (VSN). However, content security has gradually been a latent threat to the stable operation of VSN. The VSN is a time-varying environment and mixed with various real or fake contents, which brings great challenges to the sustainability of VSN. To establish a sustainable VSN, it is of practical value to possess a strong ability for fake content detection. Related works can be divided into the global semantics-based approaches and the local semantics-based approaches, though both with limitations. Leveraging these two different approaches, this paper proposes a fake content detection model based on the mixed graph neural networks (GNN) for sustainable VSN. It takes GNN as the bottom architecture and integrates both convolution neural networks and recurrent neural networks to capture two aspects of semantics. Such a mixed detection framework is expected to possess a better detection effect. A number of experiments were conducted on two social network datasets for evaluation, and the results indicated that the detection effect can be improved by about 5%-15% compared with baseline methods.
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IEEE Transactions on Intelligent Transportation SystemsPublisher DOI
ISSN
1524-9050eISSN
1558-0016Publication classification
C1 Refereed article in a scholarly journalUsage metrics
Keywords
Science & TechnologyTechnologyEngineering, CivilEngineering, Electrical & ElectronicTransportation Science & TechnologyEngineeringTransportationFake newsSemanticsConvolutional neural networksSocial networking (online)EncodingConvolutionRecurrent neural networksFake content detectionvehicular social networkssustainable solutionsgraph neural networksSPAM DETECTIONREPRESENTATIONArtificial Intelligence and Image Processing
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