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

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