Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

Machine Learning and Knowledge Extraction

Bin Zhou¹  Yan Jia¹  Liang Zhang¹  Jingqun Li² 

[1] College of Computer, National University of Defense Technology, Changsha 410073, China;Shenzhen LiCi Electronic Company, Shenzhen 518000, China;

关键词: rumor detection;    graph neural network;    artificial intelligence;   

DOI  :  10.3390/make3010005

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown