Zareie, A., Bontcheva, K. orcid.org/0000-0001-6152-9600 and Scarton, C. orcid.org/0000-0002-0103-4072 (2025) A lightweight approach for user and keyword classification in controversial topics. In: Maria Aiello, L., Chakraborty, T. and Gaito, S., (eds.) Social Networks Analysis and Mining (ASONAM 2024). The 16th International Conference on Advances in Social Networks Analysis and Mining - ASONAM-2024, 02-05 Sep 2024, Rende, Italy. Lecture Notes in Computer Science, 15212 (1). Springer Nature Switzerland , pp. 243-253. ISBN 9783031785375
Abstract
Classifying the stance of individuals on controversial topics and uncovering their concerns is crucial for social scientists and policymakers. Data from Online Social Networks (OSNs), which serve as a proxy to a representative sample of society, offers an opportunity to classify these stances, discover society’s concerns regarding controversial topics, and track the evolution of these concerns over time. Consequently, stance classification in OSNs has garnered significant attention from researchers. However, most existing methods for this task often rely on labelled data and utilise the text of users’ posts or the interactions between users, necessitating large volumes of data, considerable processing time, and access to information that is not readily available (e.g. users’ followers/followees). This paper proposes a lightweight approach for the stance classification of users and keywords in OSNs, aiming at understanding the collective opinion of individuals and their concerns. Our approach employs a tailored random walk model, requiring just one keyword representing each stance, using solely the keywords in social media posts. Experimental results demonstrate the superior performance of our method compared to the baselines, excelling in stance classification of users and keywords, with a running time that, while not the fastest, remains competitive.
Metadata
Item Type: | Proceedings Paper |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. Except as otherwise noted, this author-accepted version of a paper published in Social Networks Analysis and Mining is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Users Classification; Keyword Classification; Stance Detection; Random Walk |
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: | 07 Mar 2025 08:29 |
Last Modified: | 07 Mar 2025 08:31 |
Status: | Published |
Publisher: | Springer Nature Switzerland |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-031-78538-2_21 |
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Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:224134 |