@inproceedings{tian-etal-2022-duck,
title = "{DUCK}: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks",
author = "Tian, Lin and
Zhang, Xiuzhen and
Lau, Jey Han",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.364",
doi = "10.18653/v1/2022.naacl-main.364",
pages = "4939--4949",
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 $\underline{d}$etection with $\underline{u}$ser and $\underline{c}$omment networ$\underline{k}$s) 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: \url{https://github.com/ltian678/DUCK-code}.",
}
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<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 \underlinedetection with \underlineuser and \underlinecomment networ\underlineks) 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.</abstract>
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%0 Conference Proceedings
%T DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks
%A Tian, Lin
%A Zhang, Xiuzhen
%A Lau, Jey Han
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F tian-etal-2022-duck
%X 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 \underlinedetection with \underlineuser and \underlinecomment networ\underlineks) 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.
%R 10.18653/v1/2022.naacl-main.364
%U https://aclanthology.org/2022.naacl-main.364
%U https://doi.org/10.18653/v1/2022.naacl-main.364
%P 4939-4949
Markdown (Informal)
[DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks](https://aclanthology.org/2022.naacl-main.364) (Tian et al., NAACL 2022)
ACL