ITM Web Conf.
Volume 47, 20222022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
|Number of page(s)||6|
|Section||Algorithm Optimization and Application|
|Published online||23 June 2022|
Rumor detection based on graph attention network
1 School of Computer Science and Technology, Tiangong University, Tianjin, China
2 School of Software, Tiangong University, Tianjin, China
3 Boustead College, Tianjin University of Commerce, Tianjin, China
* Corresponding author: Sunxuemei@tiangong.edu.cn
At present, most of the existing rumor detection methods focus on the learning and fusion of various features, but due to the complexity of language, these models often rarely consider the relationship between parts of speech. This paper uses graph attention neural network model to learn text features and syntactic relations to solve this problem. It uses node attention collection text feature and edge attention collection relationship feature for syntactic dependency tree, and node attention and edge attention to enhance each other. Finally, the proposed method is verified on Twitter and Weibo data sets. The experimental results show that the proposed method has greatly improved the early detection and accuracy of rumors.
Key words: Rumor detection / Graph attention neural network / Syntactic relations / Deep learning
© The Authors, published by EDP Sciences, 2022
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