TY - JOUR AU - Gandhi, Nupoor AU - Morales, Alex AU - Chan, Sally Man-Pui AU - Albarracin, Dolores AU - Zhai, ChengXiang PY - 2020/04/03 Y2 - 2024/03/28 TI - Predicting Opioid Overdose Crude Rates with Text-Based Twitter Features (Student Abstract) JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 10 SE - Student Abstract Track DO - 10.1609/aaai.v34i10.7165 UR - https://ojs.aaai.org/index.php/AAAI/article/view/7165 SP - 13787-13788 AB - <p>Drug use reporting is often a bottleneck for modern public health surveillance; social media data provides a real-time signal which allows for tracking and monitoring opioid overdoses. In this work we focus on text-based feature construction for the prediction task of opioid overdose rates at the county level. More specifically, using a Twitter dataset with over 3.4 billion tweets, we explore semantic features, such as topic features, to show that social media could be a good indicator for forecasting opioid overdose crude rates in public health monitoring systems. Specifically, combining topic and TF-IDF features in conjunction with demographic features can predict opioid overdose rates at the county level.</p> ER -