Exploratory Analysis of Marketing and Non-marketing E-cigarette Themes on Twitter

  • Sifei Han
  • Ramakanth KavuluruEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10047)


Electronic cigarettes (e-cigs) have been gaining popularity and have emerged as a controversial tobacco product since their introduction in 2007 in the U.S. The smoke-free aspect of e-cigs renders them less harmful than conventional cigarettes and is one of the main reasons for their use by people who plan to quit smoking. The US food and drug administration (FDA) has introduced new regulations early May 2016 that went into effect on August 8, 2016. Given this important context, in this paper, we report results of a project to identify current themes in e-cig tweets in terms of semantic interpretations of topics generated with topic modeling. Given marketing/advertising tweets constitute almost half of all e-cig tweets, we first build a classifier that identifies marketing and non-marketing tweets based on a hand-built dataset of 1000 tweets. After applying the classifier to a dataset of over a million tweets (collected during 4/2015 – 6/2016), we conduct a preliminary content analysis and run topic models on the two sets of tweets separately after identifying the appropriate numbers of topics using topic coherence. We interpret the results of the topic modeling process by relating topics generated to specific e-cig themes. We also report on themes identified from e-cig tweets generated at particular places (such as schools and churches) for geo-tagged tweets found in our dataset using the GeoNames API. To our knowledge, this is the first effort that employs topic modeling to identify e-cig themes in general and in the context of geo-tagged tweets tied to specific places of interest.


Topic Modeling Sentiment Analysis Convolutional Neural Network Electronic Cigarette Electronic Nicotine Delivery System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank anonymous reviewers for constructive criticism that helped improve the presentation of this paper. 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.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Division of Biomedical Informatics, Department of Internal MedicineUniversity of KentuckyLexingtonUSA
  2. 2.Department of Computer ScienceUniversity of KentuckyLexingtonUSA

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