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

Mouth Level Intake of Nicotine from Three Brands of Little Filtered Cigars with Widely Differing Product Characteristics Among Adult Consumers

Little filtered cigars are tobacco products with many cigarette-like characteristics. However, despite cigars falling under the U.S. Food and Drug Administration regulatory authority, characterizing flavors, which are still allowed in little filtered cigars, and filter design may influence how people use the products and the resulting exposure to harmful and potentially harmful constituents. We estimated nicotine mouth level intake (MLI) from analyses of little cigar filter butt solanesol levels, brand characteristics, carbon monoxide boost, and puff volume in 48 dual cigarette/cigar users during two repeat bouts of ad lib smoking of three little filtered cigar brands. Mean nicotine MLI for the three brands was significantly different with Swisher Sweets (0.1% ventilation) Cherry at 1.20 mg nicotine, Cheyenne Menthol (1.5%) at 0.63 mg, and Santa Fe unflavored (49%) at 0.94 mg. The association between nicotine MLI and puff volume was the same between Cheyenne Menthol and Santa Fe unflavored. However, these were different from Swisher Sweets Cherry. At least five main factorsflavor, ventilation, filter design, nicotine delivery related to tar, and user puff volumemay directly or indirectly impact MLI and its association with other measures. We found that users of little filtered cigars that have different filter ventilation and flavor draw dissimilar amounts of nicotine from the product, which may be accompanied by differences in exposure to other harmful smoke constituents