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

Misinformation and Disinformation Outbreaks and Information Prevalence Studies (61) JMIR Theme Issue: COVID-19 Special Issue (2491) Theme Issue 2022: Social Media, Ethics, and COVID-19 Misinformation (45) Infodemiology and Infoveillance (1444) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1085) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1771)

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
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Xiaowen Y, Shenzhong Z, Jingjing D, Hanzhen J, Huiling P. A study on the practical logic and promotion path of agile governance of distorted health information on the internet: A review. Medicine 2025;104(13):e41897 View

Conference Proceedings

  1. Donner C, Danala G, Jentner W, Ebert D. 2024 IEEE International Conference on Big Data (BigData). TRUExT: Trustworthiness Regressor Unified Explainable Tool View