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BEAST: Leveraging Contrastive Learning and Unsupervised Sentence Embeddings for Improved Drug Abuse Detection
Shiwakoti, Shuvam Shah, Siddhant Bikram Wang, Wei Thapa, Surendrabikram Razzak, Imran
Shiwakoti, Shuvam
Shah, Siddhant Bikram
Wang, Wei
Thapa, Surendrabikram
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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Abstract
Prescription drug abuse is a growing public health crisis worldwide. In the digital age, social media platforms offer a unique opportunity to monitor drug abuse trends in real-time. However, traditional machine learning models struggle with the informal language, sarcasm, and figurative speech used on social media. This paper proposes BEAST, a novel approach that leverages contrastive learning to improve the detection of drug abuse references hidden within figurative language. Additionally, the integration of SimCSE and Target-Based Generating Strategy further enhances the model's performance by generating superior representations from both labeled and unlabeled data. We test our model on three datasets, and the experimental results demonstrate the superiority of BEAST over the baseline in accurately identifying drug-related references hidden within figurative language on social media. Our work paves the way for more effective public health interventions in this increasingly digital era. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Citation
S. Shiwakoti, S. B. Shah, W. Wang, S. Thapa, I. Razzak, and U. Naseem, “BEAST: Leveraging Contrastive Learning and Unsupervised Sentence Embeddings for Improved Drug Abuse Detection,” pp. 1938–1945, May 2025, doi: 10.1145/3701716.3717743
Source
WWW Companion 2025 - Companion Proceedings of the ACM Web Conference 2025
Conference
34th ACM Web Conference, WWW Companion 2025
Keywords
Computational Social Science, Drug Abuse Detection, Evaluation, Sentence Embeddings
Subjects
Source
34th ACM Web Conference, WWW Companion 2025
Publisher
Association for Computing Machinery