Assessing the public discourse on Twitter: Reactions to theJUUL e-cigarettes ban in the United States
Bibliographic record
Abstract
INTRODUCTION: JUUL is a high-nicotine pod-based vaping device that is popular among adolescents and young adults. On 23 June 2022, the US Food and Drug Administration (FDA) denied authorization to market JUUL, and ordered JUUL Labs to remove products from the US market. The next day, a US federal appeals court temporarily suspended the ban. The mixed public discourse surrounding the FDA ban warrants further investigation. METHODS: This study examined Twitter data to describe public reaction to these announcements. Posts containing terms 'JUUL' and/or '#JUUL' (N=97548 unique tweets) were collected from 23 June to 3 July 2022, from Twitter's Streaming Application Programming Interface (API). After removing retweets, we used an inductive approach to become familiar with the data, generated a codebook, and conducted a content analysis on a random sample of n=4000 tweets. RESULTS: A total of 2755 (68.9%) tweets discussed JUUL in the context of the FDA ban. News (n=1425/2755; 51.7%) about the JUUL ban, government distrust (n=588; 21.3%), and individual rights (n=253; 9.2%) were the most prevalent themes. Less commonly discussed themes included inconsistencies between policies (n=174; 6.3%), mentions of switching to other products (n=162; 5.9%), smoking cessation (n=99; 3.6%), and craving for JUUL (n=94; 3.4%). Sentiment analysis of JUUL ban-related posts (n=2755) demonstrated that 1989 (72.2%) tweets were categorized as neutral, while anti-ban posts (n=566; 20.5%) were more prevalent than pro-ban posts (n=200; 7.3%). CONCLUSIONS: Besides straightforward announcements of the JUUL ban and its suspension, Twitter posts discussed government distrust, individual rights, and policy inconsistencies. While most posts conveyed neutral sentiments, anti-ban posts were almost three times more prevalent than pro-ban posts. Our findings suggest that text-based social media platforms like Twitter may be an effective instrument to understand opinions, attitudes, and beliefs regarding the FDA's JUUL ban.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".