Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

Version 1 : Received: 18 November 2020 / Approved: 19 November 2020 / Online: 19 November 2020 (12:43:51 CET)
Version 2 : Received: 21 December 2020 / Approved: 22 December 2020 / Online: 22 December 2020 (14:23:02 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Mach. Learn. Knowl. Extr. 2021, 3, 5
DOI: 10.3390/make3010005

Abstract

Identifying fake news on the media has been an important issue. This is especially true considering the wide spread of rumors on the 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.

Subject Areas

rumor detection; graph neural network; artificial intelligence

Comments (1)

Comment 1
Received: 22 December 2020
Commenter: Jingqun Li
Commenter's Conflict of Interests: Author
Comment: 1: The text has been thoroughly revised;
2: The motivation for this research has been more clearly described;
3: More details about the experiment results have been added.
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