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

Behavioural Surveillance for Public Health (204) Infodemiology and Infoveillance (1269) Machine Learning (1370) Natural Language Processing (636) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1010) Smoking Cessation (514)

Published on in Vol 6, No 3 (2020): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19975, first published .
Investigating the Attitudes of Adolescents and Young Adults Towards JUUL: Computational Study Using Twitter Data

Investigating the Attitudes of Adolescents and Young Adults Towards JUUL: Computational Study Using Twitter Data

Investigating the Attitudes of Adolescents and Young Adults Towards JUUL: Computational Study Using Twitter Data

Authors of this article:

Ryzen Benson1 Author Orcid Image ;   Mengke Hu1 Author Orcid Image ;   Annie T Chen2 Author Orcid Image ;   Subhadeep Nag3 Author Orcid Image ;   Shu-Hong Zhu4 Author Orcid Image ;   Mike Conway1 Author Orcid Image
Article Authors Cited by (14) Tweetations (23) 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. 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
  3. 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
  4. Hu M, Benson R, Chen A, Zhu S, Conway M. Determining the prevalence of cannabis, tobacco, and vaping device mentions in online communities using natural language processing. Drug and Alcohol Dependence 2021;228:109016 View
  5. Lee J, Suttiratana S, Sen I, Kong G. E-Cigarette Marketing on Social Media: A Scoping Review. Current Addiction Reports 2023;10(1):29 View
  6. Singh T, Olivares S, Cohen T, Cobb N, Wang J, Franklin A, Myneni S. Pragmatics to Reveal Intent in Social Media Peer Interactions: Mixed Methods Study. Journal of Medical Internet Research 2021;23(11):e32167 View
  7. Benson R, Hu M, Chen A, Zhu S, Conway M. Examining Cannabis, Tobacco, and Vaping Discourse on Reddit: An Exploratory Approach Using Natural Language Processing. Frontiers in Public Health 2022;9 View
  8. Rutherford B, Lim C, Johnson B, Cheng B, Chung J, Huang S, Sun T, Leung J, Stjepanović D, Chan G. #TurntTrending: a systematic review of substance use portrayals on social media platforms. Addiction 2023;118(2):206 View
  9. 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
  10. Wang Y, Duan Z, Weaver S, Popova L, Spears C, Ashley D, Pechacek T, Eriksen M, Huang J. Consumption of JUUL vs. Other E-Cigarette Brands among U.S. E-Cigarette Users: Evidence from Wave 5 of the PATH Study. International Journal of Environmental Research and Public Health 2022;19(17):10837 View
  11. Purushothaman V, McMann T, Li Z, Cuomo R, Mackey T. Content and trend analysis of user-generated nicotine sickness tweets: A retrospective infoveillance study. Tobacco Induced Diseases 2022;20(March):1 View
  12. Kresovich A, Norris A, Carter C, Kim Y, Kostygina G, Emery S. Deciphering Influence on Social Media: A Comparative Analysis of Influential Account Detection Metrics in the Context of Tobacco Promotion. Social Media + Society 2024;10(1) View

Books/Policy Documents

  1. Lempert L, Halpern-Felsher B. Electronic Cigarettes and Vape Devices. View