Few Labels with Active Learning: From Weak to Strong Labels for Misinformation Detection
Alalawi, Abdulrahman Alsuhaibani, Abdullah Naseem, Usman Suleiman, Basem Jameel, Shoaib Razzak, Imran
Alalawi, Abdulrahman
Alsuhaibani, Abdullah
Naseem, Usman
Suleiman, Basem
Jameel, Shoaib
Razzak, Imran
Supervisor
Department
Computational Biology
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
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Journal Issue
Abstract
Social media has evolved into a platform where individuals can freely disseminate content online. However, this unrestricted environment has unfortunately led to its misuse, with these platforms increasingly being used to circulate inappropriate content, misinformation, and disinformation. Misinformation detection becomes a crucial task in seeking social safety. In this work, we propose a framework called Few Labels with Active Learning (FLAL), which leverages a margin-sampling technique within the active learning paradigm. This enables the model to prioritize and learn from the most informative yet uncertain instances, enhancing its performance by focusing iteratively on examples that contribute the most to learning. As a result, the need for extensive labelling efforts is reduced. We evaluate FLAL across four multilingual benchmark datasets, where it demonstrates competitive results despite utilizing only a few labelled samples. To the best of our knowledge, this is the first study to apply active learning in conjunction with the few-label paradigm to data derived from an Arabic-language context. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Citation
A. Alalawi, A. Alsuhaibani, U. Naseem, B. Suleiman, S. Jameel, and I. Razzak, “Few Labels with Active Learning: From Weak to Strong Labels for Misinformation Detection,” vol. 25, pp. 2592–2596, May 2025, doi: 10.1145/3701716.3718376
Source
WWW Companion 2025 - Companion Proceedings of the ACM Web Conference 2025
Conference
34th ACM Web Conference, WWW Companion 2025
Keywords
Active Learning, Misinformation
Subjects
Source
34th ACM Web Conference, WWW Companion 2025
Publisher
Association for Computing Machinery