The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Research on Rumor Detection Based on a Graph Attention Network With Temporal Features
|
Author(s): Xiaohui Yang (Hebei University, China), Hailong Ma (Hebei University, China & China Telecom Stocks Co., Ltd., China)and Miao Wang (Hebei University, China)
Copyright: 2023
Volume: 19
Issue: 2
Pages: 17
Source title:
International Journal of Data Warehousing and Mining (IJDWM)
Editor(s)-in-Chief: Eric Pardede (La Trobe University, Australia)and Kiki Adhinugraha (La Trobe University, Australia)
DOI: 10.4018/IJDWM.319342
Purchase
|
Abstract
The higher-order and temporal characteristics of tweet sequences are often ignored in the field of rumor detection. In this paper, a new rumor detection method (T-BiGAT) is proposed to capture the temporal features between tweets by combining a graph attention network (GAT) and gated recurrent neural network (GRU). First, timestamps are calculated for each tweet within the same event. On the premise of the same timestamp, two different propagation subgraphs are constructed according to the response relationship between tweets. Then, GRU is used to capture intralayer dependencies between sibling nodes in the subtree; global features of each subtree are extracted using an improved GAT. Furthermore, GRU is reused to capture the temporal dependencies of individual subgraphs at different timestamps. Finally, weights are assigned to the global feature vectors of different timestamp subtrees for aggregation, and a mapping function is used to classify the aggregated vectors.
Related Content
Feiqi Liu, Dong Yang, Yuyang Zhang, Chengcai Yang, Jingjing Yang.
© 2024.
19 pages.
|
Qiliang Zhu, Changsheng Wang, Wenchao Jin, Jianxun Ren, Xueting Yu.
© 2024.
17 pages.
|
JianDong He.
© 2024.
14 pages.
|
.
© 2024.
|
.
© 2024.
|
.
© 2024.
|
Man Jiang, Qilong Han, Haitao Zhang, Hexiang Liu.
© 2023.
15 pages.
|
|
|