Rumor Detection on Twitter with Tree-structured Recursive Neural Networks

Jing Ma, Wei Gao, Kam-Fai Wong


Abstract
Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based on a bottom-up and a top-down tree-structured neural networks for rumor representation learning and classification, which naturally conform to the propagation layout of tweets. Results on two public Twitter datasets demonstrate that our recursive neural models 1) achieve much better performance than state-of-the-art approaches; 2) demonstrate superior capacity on detecting rumors at very early stage.
Anthology ID:
P18-1184
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1980–1989
Language:
URL:
https://aclanthology.org/P18-1184
DOI:
10.18653/v1/P18-1184
Bibkey:
Cite (ACL):
Jing Ma, Wei Gao, and Kam-Fai Wong. 2018. Rumor Detection on Twitter with Tree-structured Recursive Neural Networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1980–1989, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks (Ma et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1184.pdf
Presentation:
 P18-1184.Presentation.pdf
Video:
 https://vimeo.com/285804974
Code
 majingCUHK/Rumor_RvNN