Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning

Quanzhi Li, Qiong Zhang, Luo Si


Abstract
In this study, we propose a new multi-task learning approach for rumor detection and stance classification tasks. This neural network model has a shared layer and two task specific layers. We incorporate the user credibility information into the rumor detection layer, and we also apply attention mechanism in the rumor detection process. The attended information include not only the hidden states in the rumor detection layer, but also the hidden states from the stance detection layer. The experiments on two datasets show that our proposed model outperforms the state-of-the-art rumor detection approaches.
Anthology ID:
P19-1113
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1173–1179
Language:
URL:
https://aclanthology.org/P19-1113
DOI:
10.18653/v1/P19-1113
Bibkey:
Cite (ACL):
Quanzhi Li, Qiong Zhang, and Luo Si. 2019. Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1173–1179, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning (Li et al., ACL 2019)
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PDF:
https://aclanthology.org/P19-1113.pdf