Published on in Vol 23, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30753, first published .
Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

Mai ElSherief   1 , PhD ;   Steven A Sumner   2 , MD ;   Christopher M Jones   3 , DPH ;   Royal K Law   4 , PhD ;   Akadia Kacha-Ochana   2 , MPH ;   Lyna Shieber   5 , DPhil ;   LeShaundra Cordier   5 , MPH ;   Kelly Holton   3 , MPH ;   Munmun De Choudhury   6 , PhD

1 University of California, San Diego, San Diego, CA, United States

2 Office of Strategy and Innovation, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States

3 National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States

4 Division of Injury Prevention, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States

5 Brunet-García, Atlanta, GA, United States

6 School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States

Corresponding Author:

  • Munmun De Choudhury, PhD
  • School of Interactive Computing
  • Georgia Institute of Technology
  • 756 W Peachtree St NW
  • Atlanta, GA, 30308
  • United States
  • Phone: 1 4043858603
  • Email: munmund@gatech.edu