Identifying current Juul users among emerging adults through Twitter feeds. (February 2021)

• Record Type: Journal Article
• Authors: Tran, Tung; Ickes, Melinda J.; Hester, Jakob W.; Kavuluru, Ramakanth
• Is Part Of: International journal of medical informatics. Volume 146(2021)
• Publication Date: 2021-02
• Subjects: e-cigarettes -- Juul -- Tobacco prevention -- Machine learning; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2020.104350
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Social media participation and influence drive tobacco product use habits and beliefs. Identifying Juul users through social data can help launch online prevention campaigns. Participants' Twitter accounts linked with survey responses were used for identification. Our results demonstrate presence of latent signals in tweets to determine current use. Friend and follower patterns have a large impact on Juul user classification performance. Abstract: Introduction: Juul is the most popular electronic cigarette on the market. Amid concerns around uptake of e-cigarettes by never smokers, can we detect whether someone uses Juul based on their social media activities? This is the central premise of the effort reported in this paper. Several recent social media-related studies on Juul use tend to focus on the characterization of Juul-related messages on social media. In this study, we assess the potential in using machine learning methods to automatically identify Juul users (past 30-day usage) based on their Twitter data. Methods: We obtained a collection of 588 instances, for training and testing, of Juul use patterns (along with associated Twitter handles) via survey responses of college students. With this data, we built and tested supervised machine learning models based on linear and deep learning algorithms with textual, social network (friends and followers), and other hand-crafted features. Results: The linear model with textual and follower network features performed best with a precision-recall trade-off such that precision (PPV) is 57 % at 24 % recall (sensitivity). Hence, at least every other college-attending Twitter user flagged by our model is expected to be a Juul user. Additionally, our results indicate that social network features tend to have a large impact (positive) on classification performance. Conclusion: There are enough latent signals from social feeds for supervised modeling of Juul use, even with limited training data, implying that such models are highly beneficial to very focused intervention campaigns. This initial success indicates potential for more involved automated surveillance of Juul use based on social media data, including Juul usage patterns, nicotine dependence, and risk awareness.