Additionally, to understand Juul's total twitter appeal, its perception across different age and gender demographics needs to be explored, a method that has not been widely explored to date. Understanding each demographic (age and gender) appeal to the product through Twitter content will allow one to compare which aspect of the Juul is most appealing to each age group. At the time of its introduction, due to product innovation for public awareness, few FDA regulations were imposed.
Having covered a brief history of Juul and how it works, let's now consider its potential effects on youth. This idea is reminiscent of the common thought pattern that signifies the end of a diet. Glantz, Ph.D. and part of the Center for Tobacco Control Research and Education addresses this question by stating 5 conclusions related to the effects of nicotine that would differ from the effects of caffeine (Stanton A. Glantz) .
HISTORY OF JUUL
In this study, I expect to see some consistency in the types of topics related to the Juul, as previous studies have shown. Hypothesis one: There is a lot of concern about the health of Juul for young people. Due to Pax Labs' recent branding approach, a successful promotional campaign would involve adults' popularity and discuss the importance of the Juul in helping them quit.
I would also expect a lack of discussion of the Juul as part of their daily lives. I'm curious to find out what age demographic most people are discussing Juul's youth health concerns. In a similar vein, I expect to see the Juul tied to stress, especially in college and high school settings.
METHODOLOGY
DATA COLLECTION
Note the highlighted area of the "text" object key, which contains the text of the tweet and the user, which has even more key-value pairs important to the method. The following image below is a screenshot of some of the data contained in the user object. These highlighted lines are just some of the "key":"value" pairs that I'm interested in.
However, the most important line right now is "Profile Picture URL", which gives me a link to the Twitterer's profile picture on the internet. As indicated by the Twitter logo in the code map in Chapter 5, 32,399 returned twitter objects were sent to the Humanizr API. Face Plus Plus analyzes “face-related attributes, including age, gender, emotion, head position, eye status, ethnicity, face image quality, and blur” (Plus, 2019).
Going back to Figure 6.2 where the profile image url is highlighted, Python grabs each of these images, resizes them to a visual format, and saves them to a file, which is then sent to the Face Plus Plus API, together. If there is an error collecting the image, an error is logged and the program continues to run. I then compared the url of their actual photo with the photo returned to me in the data and found that they were different in each case.
There was about a one to two month gap between when I collected the data and when I saved the images. I then sent the collection of images to Face Plus Plus, which returns a JSON object with facial attributes. Of all the images sent, a total of 11,984 were returned with an age and gender.
Before the data can be sent to different algorithms, it must first be preprocessed.
DATA PROCESSING
Stopwords are commonly used words such as “the”, “an”, “an” that do not add much meaning to a sentence. As a result, a sentence such as “the boy's car has different colors” can be translated as “the boy's car has a different color” (Standford.edu, 2009). For example, one guy got 4.1K re-tweets on this post: “If you shoot a four loko and then rip a juul in a bathroom, a frat boy will appear in the mirror.” The reason I found this tweet is that I will systematically look for words that seem oddly popular in my results.
A summary of some of the omitted retweets and how they would have changed the data if left in the Methodology Limitation section. Finally, I made a list of custom stop words to weed out other irrelevant terms in the data. In the table below is an example of the frequency for some of the words across the documents.
For example, if I said that "The girl over there is..." The last word of the sentence determines the sentence type. In plain English, term frequency "is a score of the frequency of the word in the current document" and conversely document frequency is a "score of how infrequently the word is across documents" (Brownlee. For each word in each document, there a tuple containing which word in the dictionary the word belongs to and its TF-IDF score.
For example, what is the probability that a specific tweet will be in the taste category. In other words, "the more people with interests in X that are in the co↵ee shop, and the more strongly "Mario" is associated with interest X (all the . other places" he "goes to), the more likely it is that " Mario is in the "co↵ee shop "due to interest X" (Chan. The distribution itself is 3D, and each dot in the distribution represents a mixture of the three subjects.
Lift (A > B) refers to the “increase in ratio” of the presence of B when A is present.
RESULTS
Then of course there are some expected hash tags in the sense that there are variations on the topic. The last other hashtag group refers to components of the Juul itself, such as “juul cartridge”. First, after personally reviewing the various tweets under the age of 18, very little of the tweets were about using the Juul for smoking cessation.
However, there is also the aspect of the celebration in which it is the pleasure of. Guys are more interested in the business side of Juul with words like "company". Finally, boys are more likely to talk about girls in the context of Juul than a girl is to talk about a boy.
Part of the comparison between the Juul and smoking includes the discussion of how the "taste" of the "mint", for example, of the Juul can be desired for the taste of tobacco. Their use of the word "tell" and "ask" shows respect for each other's opinion, most likely involving testimony about the Juul. These words are not as large a percentage of the WordCloud as they were in the previous age group.
Another possibility involves the lack of incentive for an older person to publicly disclose their use of the Juul. This shows the popularity of Juul not only in schools but also at work. Words like "rip" and "love" that appeared in earlier groups still remind us that this age group also likes to retain the characteristics of younger tweeters.
Hidden in it is the word "child", which foreshadows the main content of topic 3. In addition, the sharing ability of the Juul is emphasized, making it a good gift, or you can ask a friend for it for Christmas. Selling pods emphasizes the variability of different flavors that provide nuanced experiences with the Juul.
CONCLUSION
So what we see at each respective level is a much lower level of original energy being transferred upwards than is present at the lower levels. At the bottom of the Pyramid, represented mostly by those 18 and under where we have a base level of topics that are frankly quite crude but descriptive in terms of experience, where we see Juul mentioned in the context of nicotine addiction, its loss, parties, flirting to induce addiction, beer, types of tastes, its relation to life itself and the contexts in which juuls such as in school bathrooms. Instead of the Juul being used so much in terms of such experiences, we're starting to see the Juul being mentioned more in the context of school and college.
Because many of them are in the 19-25 age group, many of these tweets are most likely referring to college when they mention the word "school." At the next level, we see a heavy emphasis on talking about Juul in the context of work. At one point in the discussions, we see tweets talking about how you are not allowed to Juul in Pax Labs' own office.
At this point in the chain, people will talk about Juul from a more individualistic perspective rather than a more collective one as it relates to their peers. Finally at the highest level, in the over 55 age category with what little tweet information or "energy" remains, tweets that mention smoking or cigarettes are made up. In the future of this study, I would like to start looking more closely at the trends that are revealed over the course of a year instead of just a few months.
The Juul is such a complex topic and there has been great work done by the FDA and medical associations in an effort to discourage young people from becoming unnecessarily addicted to nicotine. In terms of policy, the point of entry for a teen who tries the Juul goes deeper than just his desire for a specific flavor. In this case, unless the craze goes away on its own, the policy itself doesn't seem like a long-term solution to teen Juul use.
However, the Juula topic opens up a platform for discussion about the nature of addiction itself.
