@inproceedings{67205c81001a418eab377d094fea159e,
title = "Predicting Opioid Overdose Crude Rates with Text-Based Twitter Features",
abstract = "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.",
author = "Nupoor Gandhi and Alex Morales and Chan, {Sally Man Pui} and Dolores Albarracin and Zhai, {Cheng Xiang}",
note = "Publisher Copyright: {\textcopyright} 2020 The Twenty-Fifth AAAI/SIGAI Doctoral Consortium (AAAI-20). All Rights Reserved.; 34th AAAI Conference on Artificial Intelligence, AAAI 2020 ; Conference date: 07-02-2020 Through 12-02-2020",
year = "2020",
language = "English (US)",
series = "AAAI 2020 - 34th AAAI Conference on Artificial Intelligence",
publisher = "American Association for Artificial Intelligence (AAAI) Press",
pages = "13787--13788",
booktitle = "AAAI 2020 - 34th AAAI Conference on Artificial Intelligence",
address = "United States",
}