Predicting Current Juul use among Emerging Adults through Twitter Feeds

Abstract

Introduction: Can we predict 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 whether an individual uses Juul (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 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 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 predictive performance. Conclusion: There are enough predictive 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. Moreover, this initial success indicates potential for more involved automated surveillance of Juul use based on social media data, including Juul usage patterns, nicotine dependency, and risk awareness.