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Understanding online sentiment toward waterpipe tobacco smoking by applying deep-learning language models to Twitter posts (2021-2023) in the United States.

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Bibliographic Details
Title: Understanding online sentiment toward waterpipe tobacco smoking by applying deep-learning language models to Twitter posts (2021-2023) in the United States.
Authors: Xie Z; Clinical and Translational Science Institute, University of Rochester Medical Center, Rochester, NY 14642, United States., Ye P; Goergen Institute for Data Science and Artificial Intelligence, University of Rochester, Rochester, NY 14627, United States., Wu M; Goergen Institute for Data Science and Artificial Intelligence, University of Rochester, Rochester, NY 14627, United States., Han Y; Goergen Institute for Data Science and Artificial Intelligence, University of Rochester, Rochester, NY 14627, United States., Shimazaki Y; Goergen Institute for Data Science and Artificial Intelligence, University of Rochester, Rochester, NY 14627, United States., Ross JC; Department of Health Law, Policy and Management, Boston University School of Public Health, Boston, MA 02118, United States., Sutfin EL; Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC 27101, United States., Li D; Clinical and Translational Science Institute, University of Rochester Medical Center, Rochester, NY 14642, United States.
Source: Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco [Nicotine Tob Res] 2026 Apr 27. Date of Electronic Publication: 2026 Apr 27.
Publication Type: Journal Article
Journal Info: Publisher: Oxford University Press Country of Publication: England NLM ID: 9815751 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1469-994X (Electronic) Linking ISSN: 14622203 NLM ISO Abbreviation: Nicotine Tob Res Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1469-994X
DOI:10.1093/ntr/ntag048