Published on in Vol 20, No 4 (2018): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10029, first published .
Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access

Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access

Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access

Tim Mackey 1*, MAS, PhD;  Janani Kalyanam 2*, MA, PhD;  Josh Klugman 3, BS;  Ella Kuzmenko 3, BA;  Rashmi Gupta 4, BE, MBA

1 Division of Infectious Disease and Global Public Health, Department of Anesthesiology, School of Medicine, University of California San Diego, La Jolla, CA, US

2 Department of Electrical and Computer Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, US

3 IBM Global Business Services , Washington, DC, US

4 Centers for Disease Control and Prevention, Division of Global Health Protection , Atlanta, GA, US

*these authors contributed equally

Corresponding Author:

  • Tim Mackey, MAS, PhD
  • Division of Infectious Disease and Global Public Health
  • Department of Anesthesiology, School of Medicine
  • University of California San Diego
  • 8950 Villa La Jolla Drive, A124
  • La Jolla, CA
  • US
  • Phone: 1 9514914161
  • Email: tmackey@ucsd.edu