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Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter
BACKGROUND: 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. METHOD...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4943591/ https://www.ncbi.nlm.nih.gov/pubmed/27410031 http://dx.doi.org/10.1371/journal.pone.0157304 |
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author | Clark, Eric M. Jones, Chris A. Williams, Jake Ryland Kurti, Allison N. Norotsky, Mitchell Craig Danforth, Christopher M. Dodds, Peter Sheridan |
author_facet | Clark, Eric M. Jones, Chris A. Williams, Jake Ryland Kurti, Allison N. Norotsky, Mitchell Craig Danforth, Christopher M. Dodds, Peter Sheridan |
author_sort | Clark, Eric M. |
collection | PubMed |
description | BACKGROUND: 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. METHODS: 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. RESULTS: 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’. CONCLUSIONS: 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. |
format | Online Article Text |
id | pubmed-4943591 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-49435912016-08-01 Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter Clark, Eric M. Jones, Chris A. Williams, Jake Ryland Kurti, Allison N. Norotsky, Mitchell Craig Danforth, Christopher M. Dodds, Peter Sheridan PLoS One Research Article BACKGROUND: 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. METHODS: 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. RESULTS: 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’. CONCLUSIONS: 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. Public Library of Science 2016-07-13 /pmc/articles/PMC4943591/ /pubmed/27410031 http://dx.doi.org/10.1371/journal.pone.0157304 Text en © 2016 Clark et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Clark, Eric M. Jones, Chris A. Williams, Jake Ryland Kurti, Allison N. Norotsky, Mitchell Craig Danforth, Christopher M. Dodds, Peter Sheridan Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter |
title | Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter |
title_full | Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter |
title_fullStr | Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter |
title_full_unstemmed | Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter |
title_short | Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter |
title_sort | vaporous marketing: uncovering pervasive electronic cigarette advertisements on twitter |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4943591/ https://www.ncbi.nlm.nih.gov/pubmed/27410031 http://dx.doi.org/10.1371/journal.pone.0157304 |
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