Abstract
Social media rumours, a form of misinformation, can mislead the public and cause significant economic and social disruption. Motivated by the observation that the user network — which captures who engage with a story — and the comment network — which captures how they react to it — provide complementary signals for rumour detection, in this paper, we propose DUCK (rumour ̲detection with ̲user and ̲comment networ ̲ks) for rumour detection on social media. We study how to leverage transformers and graph attention networks to jointly model the contents and structure of social media conversations, as well as the network of users who engaged in these conversations. Over four widely used benchmark rumour datasets in English and Chinese, we show that DUCK produces superior performance for detecting rumours, creating a new state-of-the-art. Source code for DUCK is available at: https://github.com/ltian678/DUCK-code.- Anthology ID:
- 2022.naacl-main.364
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4939–4949
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.364
- DOI:
- 10.18653/v1/2022.naacl-main.364
- Cite (ACL):
- Lin Tian, Xiuzhen Zhang, and Jey Han Lau. 2022. DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4939–4949, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks (Tian et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.naacl-main.364.pdf
- Code
- ltian678/duck-code
- Data
- CoAID