This paper is in the following e-collection/theme issue:

Infodemiology and Infoveillance (1274) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1570) Substance Abuse (382) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1013) Machine Learning (1385)

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

Authors of this article:

Tim Mackey1 Author Orcid Image ;   Janani Kalyanam2 Author Orcid Image ;   Josh Klugman3 Author Orcid Image ;   Ella Kuzmenko3 Author Orcid Image ;   Rashmi Gupta4 Author Orcid Image
Article Authors Cited by (61) Tweetations (30) Metrics

Altmetric

Altmetric discovers Social Media mentions. Click the ‘See more details’ link for a full report.

Dimensions

Dimensions discovers Citations. Click the ‘details’ link for a full report.

Metrics Since Publication

Download .csv
Daily | Monthly | Annually