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Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter
Journal article   Open access   Peer reviewed

Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter

Eric M Clark, Chris A Jones, Jake Ryland Williams, Allison N Kurti, Mitchell Craig Norotsky, Christopher M Danforth and Peter Sheridan Dodds
PloS one, v 11(7), pp e0157304-e0157304
13 Jul 2016
DOI: https://doi.org/10.1371/journal.pone.0157304
PMID: 27410031
Featured in Collection :   UN Sustainable Development Goals @ Drexel
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https://doi.org/10.1371/journal.pone.0157304View
Published, Version of Record (VoR) Open

Abstract

Advertising as Topic Commerce Direct-to-Consumer Advertising Electronic Nicotine Delivery Systems - economics Electronic Nicotine Delivery Systems - statistics & numerical data Humans Marketing Smoking Cessation Social Media
Twitter has become the "wild-west" of marketing and promotional strategies for advertisement agencies. Electronic cigarettes have been heavily marketed across Twitter feeds, offering discounts, "kid-friendly" flavors, algorithmically generated false testimonials, and free samples. All electronic cigarette keyword related tweets from a 10% sample of Twitter spanning January 2012 through December 2014 (approximately 850,000 total tweets) were identified and categorized as Automated or Organic by combining a keyword classification and a machine trained Human Detection algorithm. A sentiment analysis using Hedonometrics was performed on Organic tweets to quantify the change in consumer sentiments over time. Commercialized tweets were topically categorized with key phrasal pattern matching. The overwhelming majority (80%) of tweets were classified as automated or promotional in nature. The majority of these tweets were coded as commercialized (83.65% in 2013), up to 33% of which offered discounts or free samples and appeared on over a billion twitter feeds as impressions. The positivity of Organic (human) classified tweets has decreased over time (5.84 in 2013 to 5.77 in 2014) due to a relative increase in the negative words 'ban', 'tobacco', 'doesn't', 'drug', 'against', 'poison', 'tax' and a relative decrease in the positive words like 'haha', 'good', 'cool'. Automated tweets are more positive than organic (6.17 versus 5.84) due to a relative increase in the marketing words like 'best', 'win', 'buy', 'sale', 'health', 'discount' and a relative decrease in negative words like 'bad', 'hate', 'stupid', 'don't'. Due to the youth presence on Twitter and the clinical uncertainty of the long term health complications of electronic cigarette consumption, the protection of public health warrants scrutiny and potential regulation of social media marketing.

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5 Record Views
53 citations in Web of Science
60 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

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Collaboration types
Domestic collaboration
Web of Science research areas
Public, Environmental & Occupational Health
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