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. 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 \u27ban\u27, \u27tobacco\u27, \u27doesn\u27t\u27, \u27drug\u27, \u27against\u27, \u27poison\u27, \u27tax\u27 and a relative decrease in the positive words like \u27haha\u27, \u27good\u27, \u27cool\u27. Automated tweets are more positive than organic (6.17 versus 5.84) due to a relative increase in the marketing words like \u27best\u27, \u27win\u27, \u27buy\u27, \u27sale\u27, \u27health\u27, \u27discount\u27 and a relative decrease in negative words like \u27bad\u27, \u27hate\u27, \u27stupid\u27, \u27don\u27t\u27. 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

Automated Tweet Subcategory Counts.

<p>Automated Tweet Subcategory Counts.</p

Tweets from a random sample of 500 organic classified and 500 automated classified accounts were hand coded to gauge the accuracy of the detection algorithm.

<p>The feature set of each sampled individual is plotted in three dimensions. The traced box indicate the organic feature cutoff. True Positives (red) are correctly identified automatons, True Negatives (green) are correctly identified Humans, False Negatives (blue) are automatons classified as humans and False Positives (orange) are humans classified as automatons.</p

Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter - Fig 2

<p>Left: Binned User E-cigarette Keyword Tweet Distribution (2012-2014). Right: 2013 Automated Tweet Rank-Frequency Word Cloud. High frequency stop words (‘of’, ‘the’, etc.) are removed from the rank-frequency word distribution.</p

Categorical Tweet Word-shift Graphs: On the left, Organic Tweets from 2013 are the reference distribution to compare sentiments of Organic Tweets made in 2014 where we see a negative shift in the calculated average word happiness.

<p>Due to tweets tagged #EUEcig Ban, January 2014 and December 2013 are omitted. The computed average happiness (<i>h</i>_avg) decreases from 5.82 to 5.77 due to both an increase in the negative words ‘tobacco’, ‘drug’, ‘ban’, ‘poison’, and a decrease in the positive words ‘love’, ‘like’, ‘haha’, ‘cool’ among others. On the right, Organic Tweets from 2013 are the reference distribution to compare Automated Tweets from 2013. The words ‘free’ and ‘trial’ are excluded from the graph, since their high frequency and happiness scores distorts the image. With these key words included the the automated tweet <i>h</i>_avg increases from 6.17 to 6.59.</p

Smoking prevalence and trends among a U.S. national sample of women of reproductive age in rural versus urban settings.

U.S. smoking prevalence is declining at a slower rate in rural than urban settings and contributing to regional health disparities. Cigarette smoking among women of reproductive age is particularly concerning due to the potential for serious maternal and infant adverse health effects should a smoker become pregnant. The aim of the present study was to examine whether this rural-urban disparity impacts women of reproductive age (ages 15-44) including pregnant women. Data came from the ten most recent years of the U.S. National Survey on Drug Use and Health (2007-2016). We estimated prevalence of current smoking and nicotine dependence among women categorized by rural-urban residence, pregnancy status, and trends using chi-square testing and multivariable modeling while adjusting for common risk factors for smoking. Despite overall decreasing trends in smoking prevalence, prevalence was higher among rural than urban women of reproductive age overall (χ2(1) = 579.33, p < .0001) and among non-pregnant (χ2(1) = 578.0, p < .0001) and pregnant (χ2(1) = 79.69, p < .0001) women examined separately. An interaction between residence and pregnancy status showed adjusted odds of smoking among urban pregnant compared to non-pregnant women (AOR = .58, [.53 -.63]) were lower than those among rural pregnant compared to non-pregnant women (AOR = 0.75, [.62 -.92]), consistent with greater pregnancy-related smoking cessation among urban pregnant women. Prevalence of nicotine dependence was also higher in rural than urban smokers overall (χ2(2) = 790.42, p < .0001) and among non-pregnant (χ2(2) = 790.58, p < .0001) and pregnant women examined separately (χ2(2) = 63.69, p < .0001), with no significant changes over time. Associations involving residence and pregnancy status remained significant in models adjusting for covariates (ps < 0.05). Results document greater prevalence of smoking and nicotine dependence and suggest less pregnancy-related quitting among rural compared to urban women, disparities that have potential for direct, multi-generational adverse health impacts