Predicting Opioid Overdose Crude Rates with Text-Based Twitter Features (Student Abstract)

Authors

  • Nupoor Gandhi University of Illinois at Urbana-Champaign
  • Alex Morales University of Illinois at Urbana-Champaign
  • Sally Man-Pui Chan University of Illinois at Urbana-Champaign
  • Dolores Albarracin University of Illinois at Urbana-Champaign
  • ChengXiang Zhai University of Illinois at Urbana-Champaign

DOI:

https://doi.org/10.1609/aaai.v34i10.7165

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.

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Published

2020-04-03

How to Cite

Gandhi, N., Morales, A., Chan, S. M.-P., Albarracin, D., & Zhai, C. (2020). Predicting Opioid Overdose Crude Rates with Text-Based Twitter Features (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13787-13788. https://doi.org/10.1609/aaai.v34i10.7165

Issue

Section

Student Abstract Track