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

Early Results in Infodemiology and Infoveillance (115) Policy and Policy Proposals (162) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1994) Infodemiology and Infoveillance (1613) Natural Language Processing (985) Smoking Cessation (657) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1170) Smoking and Vaping in Adolescence (49) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (106)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/51327, first published .
Public Perceptions and Discussions of the US Food and Drug Administration's JUUL Ban Policy on Twitter: Observational Study

Public Perceptions and Discussions of the US Food and Drug Administration's JUUL Ban Policy on Twitter: Observational Study

Public Perceptions and Discussions of the US Food and Drug Administration's JUUL Ban Policy on Twitter: Observational Study

Authors of this article:

Pinxin Liu1 Author Orcid Image ;   Xubin Lou2 Author Orcid Image ;   Zidian Xie3 Author Orcid Image ;   Ce Shang4 Author Orcid Image ;   Dongmei Li3 Author Orcid Image
Article Authors Cited by (1) Tweetations (1) Metrics

Journals

  1. Xie Z, Ye P, Wu M, Han Y, Shimazaki Y, Ross J, Sutfin E, Li D. Understanding online sentiment toward waterpipe tobacco smoking by applying deep-learning language models to Twitter posts (2021–2023) in the United States. Nicotine & Tobacco Research 2026 View