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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1010) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1552) Infodemiology and Infoveillance (1269) Smoking Cessation (514)

Published on in Vol 3, No 3 (2017): Jul-Sept

Classification of Twitter Users Who Tweet About E-Cigarettes

Classification of Twitter Users Who Tweet About E-Cigarettes

Classification of Twitter Users Who Tweet About E-Cigarettes

Authors of this article:

Annice Kim1 Author Orcid Image ;   Thomas Miano2 Author Orcid Image ;   Robert Chew2 Author Orcid Image ;   Matthew Eggers3 Author Orcid Image ;   James Nonnemaker3 Author Orcid Image
Article Authors Cited by (22) Tweetations (56) Metrics

Journals

  1. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  2. Murphy J, Hsieh Y, Wenger M, Kim A, Chew R. Supplementing a survey with respondent Twitter data to measure e-cigarette information exposure. Information, Communication & Society 2019;22(5):622 View
  3. Staal Y, van de Nobelen S, Havermans A, Talhout R. New Tobacco and Tobacco-Related Products: Early Detection of Product Development, Marketing Strategies, and Consumer Interest. JMIR Public Health and Surveillance 2018;4(2):e55 View
  4. Albalawi Y, Nikolov N, Buckley J. Trustworthy Health-Related Tweets on Social Media in Saudi Arabia: Tweet Metadata Analysis. Journal of Medical Internet Research 2019;21(10):e14731 View
  5. Tassone J, Yan P, Simpson M, Mendhe C, Mago V, Choudhury S. Utilizing deep learning and graph mining to identify drug use on Twitter data. BMC Medical Informatics and Decision Making 2020;20(S11) View
  6. Ho R, Withanage M, Khong K. Sentiment drivers of hotel customers: a hybrid approach using unstructured data from online reviews. Asia-Pacific Journal of Business Administration 2020;12(3/4):237 View
  7. Singh T, Roberts K, Cohen T, Cobb N, Wang J, Fujimoto K, Myneni S. Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review. JMIR Public Health and Surveillance 2020;6(4):e21660 View
  8. Gao Y, Xie Z, Li D. Electronic Cigarette Users' Perspective on the COVID-19 Pandemic: Observational Study Using Twitter Data. JMIR Public Health and Surveillance 2021;7(1):e24859 View
  9. Robertson L, Joshi A, Legg T, Wellock G, Ray K, Evans-Reeves K. Exploring the Twitter activity around the eighth meeting of the Conference of the Parties to the WHO Framework Convention on Tobacco Control. Tobacco Control 2022;31(1):50 View
  10. Cochran C, Robertson L, Hoek J. Online marketing activity following New Zealand’s vaping legislation. Tobacco Control 2023;32(2):263 View
  11. Bardier C, Yang J, Li J, Mackey T. Characterizing alternative and emerging tobacco product transition of use behavior on Twitter. BMC Research Notes 2021;14(1) View
  12. Alhayan F, Pennington D, Ayouni S. Twitter use by the dementia community during COVID-19: a user classification and social network analysis. Online Information Review 2023;47(1):41 View
  13. Benítez-Andrades J, Alija-Pérez J, Vidal M, Pastor-Vargas R, García-Ordás M. Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study. JMIR Medical Informatics 2022;10(2):e34492 View
  14. Ezike N, Ames Boykin A, Dobbs P, Mai H, Primack B. Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series. JMIR Infodemiology 2022;2(2):e37412 View
  15. Lanza-Cruz I, Berlanga R, Aramburu M. Multidimensional Author Profiling for Social Business Intelligence. Information Systems Frontiers 2024;26(1):195 View
  16. Ba Y. Power Dynamics and Corporate Power in Governance Processes: Evidence From U.S. Environmental Governance Systems. The American Review of Public Administration 2022;52(3):206 View
  17. Pilipiec P, Liwicki M, Bota A. Using Machine Learning for Pharmacovigilance: A Systematic Review. Pharmaceutics 2022;14(2):266 View
  18. Yan Y, Chen C, Liu Y, Zhang Z, Xu L, Pu K. Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin. Frontiers in Public Health 2021;9 View
  19. 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 and Usability Study. JMIR Formative Research 2024;8:e47570 View

Books/Policy Documents

  1. Daouadi K, Zghal Rebaï R, Amous I. Machine Learning for Networking. View
  2. Martinez L, Tsou M, Spitzberg B. Empowering Human Dynamics Research with Social Media and Geospatial Data Analytics. View