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

arXiv: https://arxiv.org/abs/2504.12355 · HTML: https://mlsanity.com/paper/2504.12355/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 51 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Built on by (most cited first, 2 of 2)

- [Hope Speech Detection in code-mixed Roman Urdu tweets: A Positive Turn in Natural Language Processing](https://mlsanity.com/paper/2506.21583/) (2025)
- [GRACE: Graph Neural Networks for Locus-of-Care Prediction under Extreme Class Imbalance](https://mlsanity.com/paper/2510.20671/) (2025)

## Abstract (arXiv)

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.