Haouari, F. orcid.org/0000-0003-4842-2467 and Elsayed, T. (2024) Are authorities denying or supporting? Detecting stance of authorities towards rumors in Twitter. Social Network Analysis and Mining, 14 (1). 34. ISSN 1869-5450
Abstract
Several studies examined the leverage of the stance in conversational threads or news articles as a signal for rumor verification. However, none of these studies leveraged the stance of trusted authorities. In this work, we define the task of detecting the stance of authorities towards rumors in Twitter, i.e., whether a tweet from an authority supports the rumor, denies it, or neither. We believe the task is useful to augment the sources of evidence exploited by existing rumor verification models. We construct and release the first Authority STance towards Rumors (AuSTR) dataset, where evidence is retrieved from authority timelines in Arabic Twitter. The collection comprises 811 (rumor tweet, authority tweet) pairs relevant to 292 unique rumors. Due to the relatively limited size of our dataset, we explore the adequacy of existing Arabic datasets of stance towards claims in training BERT-based models for our task, and the effect of augmenting AuSTR with those datasets. Our experiments show that, despite its limited size, a model trained solely on AuSTR with a class-balanced focus loss exhibits a comparable performance to the best studied combination of existing datasets augmented with AuSTR, achieving a performance of 0.84 macro-F1 and 0.78 F1 on debunking tweets. The results indicate that AuSTR can be sufficient for our task without the need for augmenting it with existing stance datasets. Finally, we conduct a thorough failure analysis to gain insights for the future directions on the task.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | Claims; Evidence; Stance; Fact-checking; Social media |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Jan 2025 10:08 |
Last Modified: | 30 Jan 2025 10:08 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1007/s13278-023-01189-3 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:222412 |