Mixed Graph Neural Network-Based Fake News Detection for Sustainable Vehicular Social Networks

The 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 conten...

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Bibliographic Details
Published in:IEEE Transactions on Intelligent Transportation Systems Vol. 24; no. 12; pp. 15486 - 15498
Main Authors: Guo, Zhiwei, Yu, Keping, Jolfaei, Alireza, Li, Gang, Ding, Feng, Beheshti, Amin
Format: Journal Article
Language:English
Japanese
Published: New York IEEE 01.12.2023
Institute of Electrical and Electronics Engineers (IEEE)
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Convolutional neural networks
Encoding
Fake content detection
Fake news
Graph neural networks
Internet of Vehicles
Neural networks
Recurrent neural networks
Semantics
Social networking (online)
Social networks
sustainable solutions
vehicular social networks
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:The 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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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3185013