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We utilized text-mining to characterize e-cigarette advertising and marketing messages from image-focused social media brand sites, and to construct and test an algorithm for predicting brand from brand-generated social media posts. Text-tokenization was used to quantify text for use as predictors in analyses. Words most commonly used in posts differed by brand. Analyses revealed e-cigarette brands used different types of messages to appeal to social media users.

Since becoming available on the US market in , electronic cigarettes e-cigarettes quickly became a multi-billion dollar industry. The introduction of e-cigarettes to the US market was accompanied by a huge wave of online marketing efforts. E-cigarettes are widely promoted online, 3 - 5 particularly on social media websites such as Facebook, Twitter, YouTube, and Instagram.

Huang et al 6 documented extensive e-cigarette marketing on Twitter, with more than 73, tweets related to e-cigarettes captured in a 2-month period. Content analyses of e-cigarette-related tweets suggest that the majority of tweets are commercial in nature, 6 , 7 and that price promotions and discount codes are common. In the absence of marketing regulation, e-cigarette manufacturers regularly make unsubstantiated claims about the safety and use of e-cigarettes for smoking cessation.

The unregulated and widespread marketing of e-cigarettes on social media is especially troublesome due to its broad appeal to youth. Marketing on social media encourages user-interaction and engagement in ways that traditional marketing does not, 17 and as such, has the potential for greater influence on youth. E-cigarette brands exploit features unique to social media in their marketing, such as mentioning consumers directly by username on Twitter or allowing users to comment on posts, 5 thereby blurring the line between industry and peers.

Research has demonstrated that youth and young adult exposure to pro-tobacco social media is associated with more positive attitudes towards smoking, 19 increased positive perceptions of peer smoking norms, 19 , 20 greater smoking intentions, 19 and smoking behavior. Given the lack of regulation of e-cigarette marketing and the potential influence of social media marketing on youth, more research is needed to improve understanding of the nature, extent, and impact of e-cigarette marketing via social media.

However, analyzing social media data presents challenges. First, the data are largely text based, which until recently has been the purview of qualitative analytic techniques, mainly thematic or content coding. In theory, identifying themes or content in social media data is no different than identifying themes in other textual data, such as are generated from focus groups, in-depth interviews, or open-ended survey responses.

Yet, the sheer volume of data generated by social media has rendered such coding strategies difficult if not logistically impossible. Whereas traditional content coding strategies tend to be both labor-intensive and time consuming, the emerging field of text-mining offers an array of novel tools and methods suitable for analysis of large amounts of textual data such as those typically generated by social media platforms.

Indeed, a nascent but growing body of literature has begun to examine the prevalence, nature, and content of e-cigarette marketing on social media using text-mining techniques. Despite the difficulties and time involved, many studies to date have used manual content coding to identify themes or topics in e-cigarette related posts.

Some have focused on developing automatic detection algorithms for identifying e-cigarette related content, 29 detection of robotic automated versus organic human posts, 30 , 31 and detection of e-cigarette proponents. However, although use of text analytic techniques to examine e-cigarette marketing empirically on social media platforms is growing, most existing research has focused largely on Twitter.

As a result, there is a dearth of evidence about the nature and quality of e-cigarette advertising on the image-focused social media platforms most favored by youth and young adults such as Facebook and Instagram , particularly given their popularity in these populations. Analysis of brand-generated posts can reveal information relevant to several critical questions about e-cigarette marketing on social media platforms known to be heavily used by youth.

In this study, we aimed to address 2 such questions: Relative to existing studies, our study is novel in that: These 4 brands were chosen because they had relatively large market shares in the US, had a presence on image-focused social media, and were available for purchase in Texas. We focused on brands available for purchase in Texas because this study complemented a larger data collection effort regarding tobacco product use among youth and young adults undertaken by scholars at the Texas Center for Tobacco Regulatory Science TCORS.

To prepare the text for quantitative analysis, we utilized an algorithm for converting featurizing text into conventional quantitative explanatory variables proposed by Foster, Stine, and Liberman, 25 who used it to build a regression model to predict the selling price of real estate from listing content. This approach relies on vector space models VSM for deriving semantic meaning as detailed in Turney and Pantel.

Points closer together in a vector space are semantically similar, whereas points further apart are semantically different. The values of the elements in a VSM are derived from event frequencies for example the number of times a particular word appears in a particular context such as a document. The algorithm for featurizing text proposed by Foster et al 25 involves 3 steps. First, all source text is converted into word tokens, a process known as tokenization.

A word token is an instance of a word, defined as a unique sequence of characters delimited by white space. Next, a matrix is computed that counts the number of times a word token appears within each document or post in this case. This matrix is known as a document-term matrix. The leading singular vectors of this decomposition ie, unique words that appear most frequently across all documents are then used as explanatory variables in quantitative models. In this approach, the importance of words to semantic meaning are considered individually, without consideration of word-adjacencies bi-grams , or phrases tri-grams or greater.

The open source platform R v. To compute the document term matrix, word tokens based on text must be made uniform. For example, without this step, a capitalized word eg, E-cigarette would be counted as a separate word token from the same lower-case version of the word eg, e-cigarette. Therefore, all posts were cleaned using the text-mining tm package in R v.

Duplicate posts, meaningless and unrecognizable characters, and all punctuation were removed. All words were made lower-case. Finally, English stop words ie, extremely common English language words including articles and pronouns were removed. These posts and unique word tokens were used to compute a document-term matrix containing rows each representing an individual post , and unique words as columns , with cells containing the frequency of each word used in each post.

The final data frame for analysis combined information from the computed document-term matrix with information regarding social network platform Facebook, Instagram, or Pinterest , brand Blu, NJOY, Logic or Metro , and post length number of words per post. Descriptive analyses included the frequency distributions of words by brand and the mean length of posts by brand.

To assess the reach and popularity of the brand-generated social media posts in this sample, we examine the number of comments, likes, and re-shares for the posts by brand. To provide a preliminary characterization of the nature of Blu, NJOY, Logic, and Metro brand marketing messages, word clouds were created, producing a visual representation of the 25 most frequently used words by each brand in their social media posts. Thus, these words were re-tokenized to represent a single word in the ensuing analyses.

In addition, the preliminary word clouds revealed that words unique to particular brands reflected brand-sponsored events that happened to occur during data collection. To characterize the nature of the posts generated by each brand, we first conducted a multinomial logistic regression with brand as the outcome and the most frequently used words across all posts as predictors.

Brand was a categorical outcome with 4 categories Blu [chosen as the reference category based on alphabetic order], NJOY, Logic and Metro. Following the procedure outlined in Foster et al, 25 the most frequently used words were examined for potential use as predictors in the regression model. Because we were interested in our ability to differentiate the brands without the use of obvious brand identifiers, we excluded references to the brand ie, brand names , words used exclusively used by a particular brand eg, blufreedom, NJOY, etc , as well as words related to events sponsored by a particular brand that happened to occur during data collection.

The final model included 59 predictors representing the number of times each of the 59 words were used across all posts — battery ies , beach, buy, can, cartridge s , charger, cherry, cigarette s , coupon, cuban, customer, day, disposable, e-cig s , e-cigarette s , electric, electronic, eliquid, enjoy, espresso, event, flavor, find, free, freedom, friends, get, good, happy, kit, king, lounge, love, menthol, never, new, switch, one, order, pack, platinum, premium, saturday, smoking, smoke s , starter, super, today, tobacco, using, use, vanilla, vape, vapor, vaping, vapelife, vapelyfe, vaporlounge, and world.

Thus, the multinomial logistic regression provides information on the odds that a post was generated from a particular brand relative to the reference brand based upon the frequency of use of each of the 59 words, respectively. The multinomial logistic regression also generates predicted probabilities, which is the probability that each of the posts was from a particular brand based upon the model.

Following the multinomial logistic regression, we conducted a discriminant function analysis DFA. Specifically, DFA produces a maximum of N groups-1 in this case linear discriminant functions, which is a linear combination of variables calculated to maximize the proportion of variance explained by group differences. Functions are derived such that the first function represents the linear combination of variables most responsible for group differentiation, and so on.

Thus, DFA identifies whether clusters of variables significantly predict group membership in this case brand , how many clusters significantly predict group membership, which variables in this case words belong to each cluster, and the proportion of group variation accounted for by each cluster of words.

In addition, DFA allows identification of the order of importance of the words within each cluster for group differentiation. DFA also produces information regarding the percentage of posts correctly classified, and provides group centroids. Group centroids represent the group brand mean on each discriminant function, providing additional information on the relative score of each brand on each discriminant function.

Thus, group centroids serve to indicate the extent to which a brand is characterized by the words comprising each discriminant function. Data for re-posts of NJOY posts were unavailable. Preliminary word clouds were created to reveal the most frequently used words by each brand, as well as to get a sense of the different marketing approaches or brand voices of Blu, NJOY, Logic, and Metro brands.

As Figure 1 shows, each word cloud contains the 25 most frequently used words for each brand. The most frequently used words appear in the center of the word cloud and are the largest in size. Word frequency decreases as words become smaller in size and move out from the center of the word cloud. Logic appeared to focus on product purchase, while Metro focused on e-cigarette devices and their use. Prior to running the multinomial regression, we examined the correlation coefficients among the 59 words examined as predictor variables; we found no indication that collinearity was present.

Odds ratios presented in Table 1 can be interpreted such that for each unit increase in the use of a word, the odds that the post was generated from a particular brand relative to the reference brand, increase by a factor of x, where x is the odds-ratio. Single CI values within parentheses indicate identical upper and lower CI bounds. Blu is the reference category. Odds ratios OR indicate that for each increase in the use of the word, the odds that the post was generated from NJoy, Logic or Metro, relative to Blu, changes by a factor of x, where x is the odds ratio.

Relative to Blu, the odds that a post was generated by NJOY was significantly greater for 15 words and significantly lower for 10 words; the odds that a post was generated by Logic was significantly greater for 16 words and significantly lower for 8; and the odds that a post was generated by Metro was significantly greater for 19 words, and significantly lower for 8. Figure 2 shows the predicted probabilities generated from the logistic regression with Blu as the reference category.

The predicted probabilities for each brand are represented in 3 panels because they are plotted in a 3-dimensional space yet represented in 2 dimensions. The predicted probabilities represent the probability that each of the posts belongs to a particular brand based upon the model. As such, they represent how well the model was able to predict which brand a post was from because of the words used across all posts.

The clustering of posts by brand assigned colors indicates that the model was reasonably successful in predicting which brand made a particular post. However, the model did a better job of predicting which posts belonged to Blu and NJOY than predicting which posts belonged to Logic and Metro. Further details regarding how well the model predicted brand is provided via the discriminant function analysis.

Plotted predicted probabilities indicate how well the logistic regression model predicted which brand made a particular post based on the words used in that post. Blu posts are shown in blue, Logic in orange, Metro in purple, and NJoy in green. Table 2 presents the function labels, total structure coefficients, and standardized structure coefficients. Total structure coefficients are akin to factor loadings, and represent the bivariate correlation between each word and each discriminant function.

As such, they indicate which words load on which function. Standardized structure coefficients are akin to beta coefficients in regression, and represent the relationship between the word and the discriminant function controlling for all other words in the model. Thus, they reveal the relative importance of each word within the cluster of words loading on the function. Similar to latent factors, discriminant functions are labeled based on their content. Discriminant function analysis produces 2 other estimates relevant to our purposes, group centroids and percent correctly classified.

Group centroids indicate the average score for each brand on each linear discriminant function.

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We utilized text-mining to characterize e-cigarette advertising and marketing messages from image-focused social media brand sites, and to construct and test an algorithm for predicting brand from brand-generated social media posts. Text-tokenization was used to quantify text for use as predictors in analyses. Words most commonly used in posts differed by brand. Analyses revealed e-cigarette brands used different types of messages to appeal to social media users. Since becoming available on the US market in , electronic cigarettes e-cigarettes quickly became a multi-billion dollar industry.

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The projections are updated annually. Calculations are based on economic modeling by Frank Chaloupka, Ph. The projections indicate that cigarette tax increases boost state tax revenues and reduce smoking. That is because a significant cigarette tax increase more than offsets any revenue lost from the decline in pack sales caused by the price increase. Projections are based, in part, on research findings that nationally, a 10 percent cigarette price increase, if maintained against inflation, reduces youth smoking rates by 6. For the purposes of our modeling, tax avoidance refers to legal efforts by individual smokers to avoid paying taxes. This includes obtaining lower-taxed or untaxed cigarettes either across state lines, from internet retailers, from tribal vendors not subject to state taxes, or from other sources. Tax evasion refers to illegal methods of circumventing tobacco taxes, including organized criminal smuggling activity. These projections consider factors such as interstate excise tax differences and population distribution, compacts or agreements with tribal groups, and high-tech tax stamps in our calculations.

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