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

Public (e)Health, Digital Epidemiology and Public Health Informatics (646) Natural Language Processing (650) Information Models (22) Machine Learning (1401) Smoking Cessation (521)

Published on in Vol 24, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30257, first published .
Identifying Electronic Nicotine Delivery System Brands and Flavors on Instagram: Natural Language Processing Analysis

Identifying Electronic Nicotine Delivery System Brands and Flavors on Instagram: Natural Language Processing Analysis

Identifying Electronic Nicotine Delivery System Brands and Flavors on Instagram: Natural Language Processing Analysis

Authors of this article:

Rob Chew1 Author Orcid Image ;   Michael Wenger1 Author Orcid Image ;   Jamie Guillory2 Author Orcid Image ;   James Nonnemaker2 Author Orcid Image ;   Annice Kim2 Author Orcid Image
Article Authors Cited by (4) Tweetations (9) Metrics

Journals

  1. Satti F, Hussain M, Ali S, Saleem M, Ali H, Chung T, Lee S. A semantic sequence similarity based approach for extracting medical entities from clinical conversations. Information Processing & Management 2023;60(2):103213 View
  2. Ma S, Jiang S, Ling M, Lu B, Chen J, Shang C. Excise taxes and pricing activities of e-liquid products sold in online vape shops. Tobacco Control 2024;33(1):7 View
  3. Tang Q, Zhou R, Xie Z, Li D. Monitoring and Identifying Emerging e-Cigarette Brands and Flavors on Twitter: Observational Study. JMIR Formative Research 2022;6(12):e42241 View
  4. Ma S, Kaareen A, Park H, He Y, Jiang S, Qiu Z, Xie Z, Li D, Chen J, O’Connor R, Fong G, Shang C. How to Identify E-cigarette Brands Available in the United States during 2020–2022: Development of a Semantic Database (Preprint). JMIR Formative Research 2023 View