AI-Based Identification of Drug Use and Overdose Signals on Social Media

Sudhakar Kumar, Sarjana Singh, S.P. Pathak, Ryanveer Singh, Pooja Rai, Varsha Arya, Ching-Hsien Hsu, Brij B. Gupta

The rising prevalence of substance abuse and overdose incidents underscores the need for real-time public health surveillance. Social media offers valuable signals for monitoring these events; however, noisy language, slang usage, and class imbalance present significant challenges for automated analysis. To address these issues, the authors propose ATTEND, a multi-task neural network for substance classification and detection of 18 overdose symptoms, with symptom normalization to standardized MedDRA concepts. ATTEND was trained on a large multi-source corpus combining ADE Corpus V2 and the UCI Drug Review Dataset, comprising over 100,000 samples designed to emulate realistic social media communication. Experimental results show that ATTEND achieved 93.23% accuracy and 93.41% weighted-F1 for substance classification, 94.10% micro-F1 for overdose symptom detection, and 90.42% accuracy for symptom normalization, outperforming baseline multi-task models across all tasks. The framework is scalable, privacy-preserving, and suitable for real-time monitoring of drug abuse signals.