Leveraging Large Language Models for Multi-Class and Multi-Label Detection of Drug Use and Overdose Symptoms on Social Media
Journal:
arXiv
Published Date:
Apr 16, 2025
Abstract
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.
