Rumor detection based on graph attention network
Year: 2022
Type: conference-paper
Abstract: 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.
Source: ITM Web of Conferences
Authors: Yuwei Lv, Xuemei Sun, Yonggang Wen, Wanru Wang
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4285299448