Advertisement

Predicting Opioid Epidemic by Using Twitter Data

  • Yubao WuEmail author
  • Pavel Skums
  • Alex Zelikovsky
  • David Campo Rendon
  • Xueting Liao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10847)

Abstract

Opioid crisis was declared as a public health emergency in 2017 by the President of USA. According to the Centers for Disease Control and Prevention, more than 91 Americans die every day from an opioid overdose. Nearly $4B is provided to address the opioid epidemic in the 2018 spending bill and help fulfill the President’s Opioid Initiative.

How to monitor and predict the opioid epidemic accurately and in real time? The traditional methods mainly use the hospital data and usually have a lag of several years. Even though they are accurate, the long lag period prevents us from monitoring and predicting the epidemic in real time. We observe that people discuss things related to the epidemic a lot in social media platforms. These user behavior data collected from social media platforms can potentially help us monitor and predict the epidemic in real time.

In this paper, we study how to use Twitter to monitor the epidemic. We collect the historic tweets containing the set of keywords related to the epidemic. We count the frequency of the tweets posted at each month and each state. We compare the frequency values with the real-world death rates at each month and each state. We identify high correlation between tweet frequency values and real-world death rates. The statistical significance demonstrates that the Twitter data can be used for predicting the death rate and epidemic in future.

Notes

Acknowledgements

AZ was partially supported by NSF Grant CCF-1619110.

References

  1. 1.
    Peters, P.J., Pontones, P., Hoover, K.W., Patel, M.R., Galang, R.R., Shields, J., Blosser, S.J., Spiller, M.W., Combs, B., Switzer, W.M., et al.: HIV infection linked to injection use of oxymorphone in Indiana, 2014–2015. New Engl. J. Med. 375(3), 229–239 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yubao Wu
    • 1
    Email author
  • Pavel Skums
    • 1
    • 2
  • Alex Zelikovsky
    • 1
  • David Campo Rendon
    • 2
  • Xueting Liao
    • 1
  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA
  2. 2.Centers for Disease Control and PreventionAtlantaUSA

Personalised recommendations