| Resumen |
The opioid drug overdose death rate remains a significant public health crisis in the U.S., where an opioid epidemic has led to a dramatic rise in overdose deaths over the past two decades. Since 1999, opioids have been implicated in approximately 75% of the nearly one million drug-related deaths. Research indicates that the epidemic is caused by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and social isolation. Impeding this research is the lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolution. To address this issue, we sourced data from Reddit, where people share self-reported experiences with opioid substances, specifically using opioid drugs through different routes of administration. To achieve this objective, an opioid overdose dataset is created and manually annotated in binary and multi-classification, along with detailed annotation guidelines. In traditional manual investigations, the route of administration is determined solely through biological laboratory testing. This study investigates the efficacy of an automated tool leveraging natural language processing and transformer model, such as RoBERTa, to analyze patterns of substance use. By systematically examining these patterns, the model contributes to public health surveillance efforts, facilitating the identification of at-risk populations and informing the development of targeted interventions. This approach ultimately aims to enhance prevention and treatment strategies for opioid misuse through data-driven insights. The findings show that our proposed methodology achieved the highest cross-validation score of 93% for binary classification and 91% for multi-class classification, demonstrating performance improvements of 9.41% and 10.98%, respectively, over the baseline model (XGB, 85% in binary class and 81% in multi-class). © 2025 by the authors. |
