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

Infodemiology and Infoveillance (1349) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Machine Learning (1465) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Mass Media/Social Media Communication and Campaigns (461) Smoking Cessation (556)

Published on in Vol 22, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17478, first published .
Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

Authors of this article:

Shyam Visweswaran1, 2 Author Orcid Image ;   Jason B Colditz3 Author Orcid Image ;   Patrick O’Halloran1 Author Orcid Image ;   Na-Rae Han4 Author Orcid Image ;   Sanya B Taneja2 Author Orcid Image ;   Joel Welling5 Author Orcid Image ;   Kar-Hai Chu3 Author Orcid Image ;   Jaime E Sidani3 Author Orcid Image ;   Brian A Primack6 Author Orcid Image
Article Authors Cited by (20) Tweetations (28) Metrics

Journals

  1. 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
  2. Hswen Y, Yom-Tov E. Analysis of a Vaping-Associated Lung Injury Outbreak through Participatory Surveillance and Archival Internet Data. International Journal of Environmental Research and Public Health 2021;18(15):8203 View
  3. Wu D, Kasson E, Singh A, Ren Y, Kaiser N, Huang M, Cavazos-Rehg P. Topics and Sentiment Surrounding Vaping on Twitter and Reddit During the 2019 e-Cigarette and Vaping Use–Associated Lung Injury Outbreak: Comparative Study. Journal of Medical Internet Research 2022;24(12):e39460 View
  4. Baker W, Colditz J, Dobbs P, Mai H, Visweswaran S, Zhan J, Primack B. Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study. JMIR Medical Informatics 2022;10(7):e33678 View
  5. Fu R, Kundu A, Mitsakakis N, Elton-Marshall T, Wang W, Hill S, Bondy S, Hamilton H, Selby P, Schwartz R, Chaiton M. Machine learning applications in tobacco research: a scoping review. Tobacco Control 2023;32(1):99 View
  6. Singh T, Roberts K, Cohen T, Cobb N, Franklin A, Myneni S. Discerning conversational context in online health communities for personalized digital behavior change solutions using Pragmatics to Reveal Intent in Social Media (PRISM) framework. Journal of Biomedical Informatics 2023;140:104324 View
  7. Sauvayre R, Vernier J, Chauvière C. An Analysis of French-Language Tweets About COVID-19 Vaccines: Supervised Learning Approach. JMIR Medical Informatics 2022;10(5):e37831 View
  8. Kong G, Schott A, Lee J, Dashtian H, Murthy D. Understanding e-cigarette content and promotion on YouTube through machine learning. Tobacco Control 2023;32(6):739 View
  9. 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
  10. Chu K, Hershey T, Hoffman B, Wolynn R, Colditz J, Sidani J, Primack B. Puff Bars, Tobacco Policy Evasion, and Nicotine Dependence: Content Analysis of Tweets. Journal of Medical Internet Research 2022;24(3):e27894 View
  11. Malik A, Berggren W, Al-Busaidi A. Instagram as a research tool for examining tobacco-related content: A methodological review. Technology in Society 2022;70:102008 View
  12. Hudon A, Beaudoin M, Phraxayavong K, Dellazizzo L, Potvin S, Dumais A. Use of Automated Thematic Annotations for Small Data Sets in a Psychotherapeutic Context: Systematic Review of Machine Learning Algorithms. JMIR Mental Health 2021;8(10):e22651 View
  13. Ren Y, Wu D, Singh A, Kasson E, Huang M, Cavazos-Rehg P. Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning Classification. Frontiers in Big Data 2022;5 View
  14. Kasson E, Singh A, Huang M, Wu D, Cavazos-Rehg P. Using a mixed methods approach to identify public perception of vaping risks and overall health outcomes on Twitter during the 2019 EVALI outbreak. International Journal of Medical Informatics 2021;155:104574 View
  15. Dupuy-Zini A, Audeh B, Gérardin C, Duclos C, Gagneux-Brunon A, Bousquet C. Users’ Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts. Journal of Medical Internet Research 2023;25:e37237 View
  16. Fisher A, Young M, Payer D, Pacheco K, Dubeau C, Mago V. Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework. Journal of Medical Internet Research 2023;25:e43630 View
  17. Liu Y, Wu J, Zhou J, Guo J, Liang C, Xing Y, Wang Z, Chen L, Ding Y, Ren D, Bai Y, Hu D. Identification of high-risk population of pneumoconiosis using deep learning segmentation of lung 3D images and radiomics texture analysis. Computer Methods and Programs in Biomedicine 2024;244:108006 View
  18. Chaudhary L, Girdhar N, Sharma D, Andreu-Perez J, Doucet A, Renz M. A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities. IEEE Transactions on Computational Social Systems 2024;11(3):3550 View

Books/Policy Documents

  1. Jacobson S, Jokela J. Artificial Intelligence for Healthcare. View
  2. . Artificial Intelligence for Healthcare. View