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.

中文翻译：

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通过 Twitter 信息流识别新兴成年人中当前的 Juul 用户

介绍

Juul是市场上最受欢迎的电子烟。由于对从不吸烟者吸食电子烟的担忧，我们能否根据某人的社交媒体活动来检测他们是否使用 Juul？这是本文所报告的工作的中心前提。最近几项关于 Juul 使用的社交媒体相关研究倾向于关注社交媒体上 Juul 相关消息的特征。在这项研究中，我们评估了使用机器学习方法根据 Juul 用户的 Twitter 数据自动识别 Juul 用户（过去 30 天的使用情况）的潜力。

方法

我们通过大学生的调查回复获得了 588 个 Juul 使用模式（以及相关 Twitter 句柄）实例的集合，用于训练和测试。利用这些数据，我们构建并测试了基于线性和深度学习算法的监督机器学习模型，具有文本、社交网络（朋友和关注者）和其他手工制作的功能。

结果

具有文本和关注者网络特征的线性模型在精确率与召回率权衡下表现最佳，使得精确率 (PPV) 为 57%，召回率为 24%（灵敏度）。因此，至少我们的模型标记的所有其他在读大学的 Twitter 用户预计都是 Juul 用户。此外，我们的结果表明社交网络特征往往对分类性能产生很大（积极）影响。

结论

即使训练数据有限，来自社交源的潜在信号也足以对 Juul 使用进行监督建模，这意味着此类模型对于非常有针对性的干预活动非常有益。这一初步成功表明，基于社交媒体数据（包括 Juul 使用模式、尼古丁依赖和风险意识）对 Juul 使用进行更多自动化监控的潜力。