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Open AccessArticle

Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

by 1, 2,*, 1 and 1
1
College of Computer, National University of Defense Technology, Changsha 410073, China
2
Shenzhen LiCi Electronic Company, Shenzhen 518000, China
*
Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2021, 3(1), 84-94; https://doi.org/10.3390/make3010005
Received: 18 November 2020 / Revised: 21 December 2020 / Accepted: 31 December 2020 / Published: 4 January 2021
(This article belongs to the Section Learning)
Identifying fake news on media has been an important issue. This is especially true considering the wide spread of rumors on popular social networks such as Twitter. Various kinds of techniques have been proposed for automatic rumor detection. In this work, we study the application of graph neural networks for rumor classification at a lower level, instead of applying existing neural network architectures to detect rumors. The responses to true rumors and false rumors display distinct characteristics. This suggests that it is essential to capture such interactions in an effective manner for a deep learning network to achieve better rumor detection performance. To this end we present a simplified aggregation graph neural network architecture. Experiments on publicly available Twitter datasets demonstrate that the proposed network has performance on a par with or even better than that of state-of-the-art graph convolutional networks, while significantly reducing the computational complexity. View Full-Text
Keywords: rumor detection; graph neural network; artificial intelligence rumor detection; graph neural network; artificial intelligence
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MDPI and ACS Style

Zhang, L.; Li, J.; Zhou, B.; Jia, Y. Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks. Mach. Learn. Knowl. Extr. 2021, 3, 84-94. https://doi.org/10.3390/make3010005

AMA Style

Zhang L, Li J, Zhou B, Jia Y. Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks. Machine Learning and Knowledge Extraction. 2021; 3(1):84-94. https://doi.org/10.3390/make3010005

Chicago/Turabian Style

Zhang, Liang; Li, Jingqun; Zhou, Bin; Jia, Yan. 2021. "Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks" Mach. Learn. Knowl. Extr. 3, no. 1: 84-94. https://doi.org/10.3390/make3010005

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