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 , US

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

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

*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
  • US
  • Phone: 1 859-302-0189
  • Email: jason.turner@louisville.edu