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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: May 7, 2020
Date Accepted: Aug 10, 2020

The final, peer-reviewed published version of this preprint can be found here:

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

Benson R, Hu M, Chen AT, Nag S, Zhu SH, Conway M

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

JMIR Public Health Surveill 2020;6(3):e19975

DOI: 10.2196/19975

PMID: 32876579

PMCID: 7495253

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

  • Ryzen Benson; 
  • Mengke Hu; 
  • Annie T. Chen; 
  • Subhadeep Nag; 
  • Shu-Hong Zhu; 
  • Mike Conway

ABSTRACT

Background:

Increases in Electronic Nicotine Delivery System (ENDS) use among high school students from 2017-2019 appear to be associated with the increasing popularity of the ENDS device JUUL.

Objective:

We employ natural language processing techniques using Twitter data to understand salient themes regarding JUUL use on Twitter and sentiment towards JUUL, as well as explore underage JUUL use.

Methods:

11,556 unique tweets containing a JUUL-related keyword were collected between July 2018 and August 2019. 4,000 tweets were manually annotated for JUUL-related themes of use and sentiment. Three machine learning algorithms were employed to classify positive/negative JUUL sentiment as well as underage JUUL mentions.

Results:

Of the annotated tweets, 79% contained a specific mention of JUUL. The most prevalent category was first-person experience (57%) and there was more positive (33%) than negative (22%) sentiment. In classification, the random forest was the best performing algorithm among all three classification tasks (i.e. positive sentiment, negative sentiment and underage JUUL mentions).

Conclusions:

A vast majority of Twitter users do not mention the potential health benefits or detriments of JUUL use. Using machine learning algorithms to identify tweets containing underage JUUL mentions can support the timely surveillance of JUUL habits and opinions, further assisting youth-targeted public health intervention strategies.


 Citation

Please cite as:

Benson R, Hu M, Chen AT, Nag S, Zhu SH, Conway M

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

JMIR Public Health Surveill 2020;6(3):e19975

DOI: 10.2196/19975

PMID: 32876579

PMCID: 7495253

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