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

Misinformation and Disinformation Outbreaks and Information Prevalence Studies (47) JMIR Theme Issue: COVID-19 Special Issue (2461) Theme Issue: Social Media, Ethics, and COVID-19 Misinformation (37) Infodemiology and Infoveillance (1369) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1050) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1667)

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

Authors of this article:

Jason Turner1 Author Orcid Image ;   Mehmed Kantardzic1 Author Orcid Image ;   Rachel Vickers-Smith2 Author Orcid Image ;   Andrew G Brown3 Author Orcid Image
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