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, United States
2
Department of Electrical and Computer Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, United States
3
IBM Global Business Services, Washington, DC, United States
4
Centers for Disease Control and Prevention, Division of Global Health Protection, Atlanta, GA, United States
*these authors contributed equally
Corresponding Author:
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Tim Mackey, MAS, PhD
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Division of Infectious Disease and Global Public Health
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Department of Anesthesiology, School of Medicine
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University of California San Diego
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8950 Villa La Jolla Drive, A124
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La Jolla, CA, 92037
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United States
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Phone:
1 9514914161
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Email: tmackey@ucsd.edu