Abstract
Rumors are manufactured with no respect for accuracy, but can circulate quickly and widely by “word-of-post” through social media conversations. Conversation tree encodes important information indicative of the credibility of rumor. Existing conversation-based techniques for rumor detection either just strictly follow tree edges or treat all the posts fully-connected during feature learning. In this paper, we propose a novel detection model based on tree transformer to better utilize user interactions in the dialogue where post-level self-attention plays the key role for aggregating the intra-/inter-subtree stances. Experimental results on the TWITTER and PHEME datasets show that the proposed approach consistently improves rumor detection performance.- Anthology ID:
- 2020.coling-main.476
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 5455–5466
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.476
- DOI:
- 10.18653/v1/2020.coling-main.476
- Cite (ACL):
- Jing Ma and Wei Gao. 2020. Debunking Rumors on Twitter with Tree Transformer. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5455–5466, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Debunking Rumors on Twitter with Tree Transformer (Ma & Gao, COLING 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.476.pdf