Toward automated e-cigarette surveillance: Spotting e-cigarette proponents on Twitter

J Biomed Inform. 2016 Jun;61:19-26. doi: 10.1016/j.jbi.2016.03.006. Epub 2016 Mar 11.

Abstract

Background: Electronic cigarettes (e-cigarettes or e-cigs) are a popular emerging tobacco product. Because e-cigs do not generate toxic tobacco combustion products that result from smoking regular cigarettes, they are sometimes perceived and promoted as a less harmful alternative to smoking and also as means to quit smoking. However, the safety of e-cigs and their efficacy in supporting smoking cessation is yet to be determined. Importantly, the federal drug administration (FDA) currently does not regulate e-cigs and as such their manufacturing, marketing, and sale is not subject to the rules that apply to traditional cigarettes. A number of manufacturers, advocates, and e-cig users are actively promoting e-cigs on Twitter.

Objective: We develop a high accuracy supervised predictive model to automatically identify e-cig "proponents" on Twitter and analyze the quantitative variation of their tweeting behavior along popular themes when compared with other Twitter users (or tweeters).

Methods: Using a dataset of 1000 independently annotated Twitter profiles by two different annotators, we employed a variety of textual features from latest tweet content and tweeter profile biography to build predictive models to automatically identify proponent tweeters. We used a set of manually curated key phrases to analyze e-cig proponent tweets from a corpus of over one million e-cig tweets along well known e-cig themes and compared the results with those generated by regular tweeters.

Results: Our model identifies e-cig proponents with 97% precision, 86% recall, 91% F-score, and 96% overall accuracy, with tight 95% confidence intervals. We find that as opposed to regular tweeters that form over 90% of the dataset, e-cig proponents are a much smaller subset but tweet two to five times more than regular tweeters. Proponents also disproportionately (one to two orders of magnitude more) highlight e-cig flavors, their smoke-free and potential harm reduction aspects, and their claimed use in smoking cessation.

Conclusions: Given FDA is currently in the process of proposing meaningful regulation, we believe our work demonstrates the strong potential of informatics approaches, specifically machine learning, for automated e-cig surveillance on Twitter.

Keywords: Electronic cigarettes; Text classification; Text mining.

MeSH terms

  • Commerce / statistics & numerical data*
  • Data Curation*
  • Electronic Nicotine Delivery Systems*
  • Humans
  • Smoking
  • Smoking Cessation
  • Social Control, Formal
  • Social Media*
  • Terminology as Topic*
  • United States
  • United States Food and Drug Administration