Leveraging Large Language Models for Multi-Class and Multi-Label Detection of Drug Use and Overdose Symptoms on Social Media

Fuente: arXiv

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Bibliographische Detailangaben
Hauptverfasser:	Ahmad, Muhammad, Ullah, Fida, Usman, Muhammad, Habiba, Umyh, Batyrshin, ldar, Sidorov, Grigori
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Computation and Language Artificial Intelligence Social and Information Networks
Online-Zugang:	Acceder al recurso
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author	Ahmad, Muhammad Ullah, Fida Usman, Muhammad Habiba, Umyh Batyrshin, ldar Sidorov, Grigori
author_facet	Ahmad, Muhammad Ullah, Fida Usman, Muhammad Habiba, Umyh Batyrshin, ldar Sidorov, Grigori
contents	Drug overdose remains a critical global health issue, often driven by misuse of opioids, painkillers, and psychiatric medications. Traditional research methods face limitations, whereas social media offers real-time insights into self-reported substance use and overdose symptoms. This study proposes an AI-driven NLP framework trained on annotated social media data to detect commonly used drugs and associated overdose symptoms. Using a hybrid annotation strategy with LLMs and human annotators, we applied traditional ML models, neural networks, and advanced transformer-based models. Our framework achieved 98% accuracy in multi-class and 97% in multi-label classification, outperforming baseline models by up to 8%. These findings highlight the potential of AI for supporting public health surveillance and personalized intervention strategies.
format	Preprint
id	arxiv_https___arxiv_org_abs_2504_12355
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Leveraging Large Language Models for Multi-Class and Multi-Label Detection of Drug Use and Overdose Symptoms on Social Media Ahmad, Muhammad Ullah, Fida Usman, Muhammad Habiba, Umyh Batyrshin, ldar Sidorov, Grigori Computation and Language Artificial Intelligence Social and Information Networks Drug overdose remains a critical global health issue, often driven by misuse of opioids, painkillers, and psychiatric medications. Traditional research methods face limitations, whereas social media offers real-time insights into self-reported substance use and overdose symptoms. This study proposes an AI-driven NLP framework trained on annotated social media data to detect commonly used drugs and associated overdose symptoms. Using a hybrid annotation strategy with LLMs and human annotators, we applied traditional ML models, neural networks, and advanced transformer-based models. Our framework achieved 98% accuracy in multi-class and 97% in multi-label classification, outperforming baseline models by up to 8%. These findings highlight the potential of AI for supporting public health surveillance and personalized intervention strategies.
title	Leveraging Large Language Models for Multi-Class and Multi-Label Detection of Drug Use and Overdose Symptoms on Social Media
topic	Computation and Language Artificial Intelligence Social and Information Networks
url	https://arxiv.org/abs/2504.12355