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

Toward Automated E-Cigarette Surveillance: Spotting E-Cigarette Proponents on Twitter

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Toward Automated E-Cigarette Surveillance: Spotting E-Cigarette Proponents on Twitter

Ramakanth Kavuluru et al. J Biomed Inform. .
Free PMC article

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.

Figures

Figure 1
Figure 1
E-cigarette proponent classification pipeline

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