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“Comments Matter and The More The Better!”: Improving Rumor Detection with User Comments

Xu, Yang, Guo, Jie, Qiu, Weidong, Huang, Zheng, Altuncu, Enes, Li, Shujun (2023) “Comments Matter and The More The Better!”: Improving Rumor Detection with User Comments. In: Proceedings of the 2022 21st IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2022). . pp. 383-390. IEEE ISBN 978-1-66549-425-0. (doi:10.1109/TrustCom56396.2022.00060) (KAR id:97593)


While many online platforms bring great benefits to their users by allowing user-generated content, they have also facilitated generation and spreading of harmful content such as rumors. Researcher have proposed different rumor detection methods based on features extracted from the original post and/or associated comments, but how comments affect the performance of such methods remains largely less understood. In this paper, we first propose a new BERT-based rumor detection method that can outperform other state-of-the-art methods, and then used it to study the role of comments in rumor detection. Our proposed method concatenates the original post and associated comments to form a single long text, which is then segmented into shorter chunks more suitable for BERT-based vectorization. Features extracted from all trunks are fed into a classifier based on an LSTM network or a transformer layer for the classification task. The experimental results on the PHEME and Ma-Weibo datasets proved the superior performance of our method. We conducted additional experiments on different settings of our proposed method to study different aspects of the role comments play in the rumor detection task. These additional experiments led to some very interesting findings, including the surprising result that fixed-length segmentation is better than natural segmentation, and the observation that including more comments can help improve the rumor detector's performance. Some of these findings have profound operational implications for online platforms, e.g., commentators can contribute to rumor detection positively so online platforms can leverage the crowd intelligence to detect online rumors more effectively without applying over-strict content consensus policies.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/TrustCom56396.2022.00060
Additional information: © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled keywords: Rumor Detection, Social Media, BERT, Transformer, Comments
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK5101 Telecommunications > TK5105.888 World Wide Web
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
University-wide institutes > Institute of Cyber Security for Society
Depositing User: Shujun Li
Date Deposited: 25 Oct 2022 13:48 UTC
Last Modified: 31 Mar 2023 13:15 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Altuncu, Enes.

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Li, Shujun.

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