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

Ramakanth Kavuluru, A. K.M. Sabbir

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


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.

Original languageEnglish
Pages (from-to)19-26
Number of pages8
JournalJournal of Biomedical Informatics
StatePublished - Jun 1 2016

Bibliographical note

Funding Information:
We thank anonymous reviewers for their valuable comments and constructive criticism that helped improve the quality of this paper. Many thanks to Ellen Hahn of the College of Nursing at UKY for general discussions on e-cig themes and search terms. This research was supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, US National Institutes of Health (NIH), through Grant UL1TR000117 and the Kentucky Lung Cancer Research Program through Grant PO2-415–1400004000-1 . The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Publisher Copyright:
© 2016 Elsevier Inc..


  • Electronic cigarettes
  • Text classification
  • Text mining

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications


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