DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks

Lin Tian, Xiuzhen Zhang, Jey Han Lau


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 \textit{who} engage with a story — and the comment network — which captures \textit{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/l tian678/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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.364.pdf
Software:
 2022.naacl-main.364.software.zip
Code
 ltian678/duck-code
Data
CoAID