At the speed of Juul: measuring the Twitter conversation related to ENDS and Juul across space and time (2017–2018)
Bibliographic record
Abstract
Background Electronic nicotine delivery systems (ENDS) are the most-used tobacco product by adolescents, and Juul has rapidly become the most popular ENDS brand. Evidence indicates that Juul has been marketed heavily on social media. In light of recent lawsuits against the FDA spurred by claims that the agency responded inadequately to this marketing push, measuring the social media conversation about ENDS like Juul has important public health implications. Methods We employed search filters to collect Juul-related and other ENDS-related data from Twitter in 2017–2018 using Gnip Historic PowerTrack. Trained coders labelled random samples for Juul and ENDS relevance, and the labelled samples were used to train a supervised learning classifier to filter out irrelevant tweets. Tweets were geolocated into US counties and their fitness for use was assessed. Results The amount of Juul-related tweets increased 67 times over the study period (from 18 849 in the first quarter of 2017 to 1 287 028 in the last quarter of 2018), spreading widely across US counties. By the last quarter 2018, 34% of US counties had more than 6 Juul-related posts per 10 000 people, up from 0% in the first quarter 2017. However, during the same period, the total of non-Juul ENDS-related tweets decreased by 25%. Conclusions Juul-related content grew exponentially on Twitter and spread across the entire country during the time when the brand was gaining market share. This social media buzz continued to increase even after FDA’s multiple interventions to curb promotions targeting minors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".