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

Overview of attention for article published in Journal of Biomedical Informatics, June 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#11 of 1,707)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
1 news outlet
twitter
83 tweeters
facebook
2 Facebook pages
googleplus
2 Google+ users

Citations

dimensions_citation
47 Dimensions

Readers on

mendeley
122 Mendeley
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Title
Toward automated e-cigarette surveillance: Spotting e-cigarette proponents on Twitter
Published in
Journal of Biomedical Informatics, June 2016
DOI 10.1016/j.jbi.2016.03.006
Pubmed ID
Authors

Ramakanth Kavuluru, A.K.M. Sabbir

Abstract

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. 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). 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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 83 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 122 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ireland 1 <1%
Unknown 121 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 15%
Student > Master 17 14%
Student > Bachelor 13 11%
Student > Ph. D. Student 13 11%
Student > Doctoral Student 8 7%
Other 29 24%
Unknown 24 20%
Readers by discipline Count As %
Medicine and Dentistry 18 15%
Computer Science 15 12%
Social Sciences 11 9%
Nursing and Health Professions 10 8%
Psychology 9 7%
Other 25 20%
Unknown 34 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 57. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 June 2019.
All research outputs
#523,323
of 19,996,019 outputs
Outputs from Journal of Biomedical Informatics
#11
of 1,707 outputs
Outputs of similar age
#11,379
of 275,346 outputs
Outputs of similar age from Journal of Biomedical Informatics
#3
of 62 outputs
Altmetric has tracked 19,996,019 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,707 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 275,346 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 62 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.