Published on in Vol 3 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38390, first published .
Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification

Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification

Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification

Jason Turner   1 * , MS ;   Mehmed Kantardzic   1 * , PhD ;   Rachel Vickers-Smith   2 * , MPH, PhD ;   Andrew G Brown   3 * , PhD

1 Data Mining Lab, Department of Computer Science and Engineering, J B Speed School of Engineering, University of Louisville, Louisville, KY, United States

2 Department of Epidemiology and Environmental Health, College of Public Health, University of Kentucky, Lexington, KY, United States

3 Department of Criminology and Criminal Justice, Northern Arizona University, Tempe, AZ, United States

*all authors contributed equally

Corresponding Author:

  • Jason Turner, MS
  • Data Mining Lab
  • Department of Computer Science and Engineering
  • J B Speed School of Engineering, University of Louisville
  • N/A
  • Louisville, KY, 40292
  • United States
  • Phone: 1 859-302-0189
  • Email: jason.turner@louisville.edu