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
- 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)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/P18-1184.pdf
- Code
- majingCUHK/Rumor_RvNN