Response to the Netflix Docuseries “Big Vape: The Rise and Fall of JUUL”: Mixed Methods Analysis of YouTube Comments Using Qualitative Coding and Topic Modeling
Bibliographic record
Abstract
Background: On October 11, 2023, Netflix released the docuseries "Big Vape: The Rise and Fall of JUUL," which chronicled the founding of JUUL, its rise in popularity among youth, and the subsequent public backlash. The official Netflix YouTube channel posted a trailer promoting the docuseries and an official clip from the docuseries. Recent studies have demonstrated the utility of using comments posted under YouTube videos to analyze reactions to the content and discourse around the health topics explored in the video. Objective: This study aimed to (1) systematically characterize nicotine and tobacco product (NTP)-related comments and replies posted in response to the docuseries trailer and video clip and (2) explore integration of automated topic modeling techniques with traditional human-generated qualitative coding. Methods: We extracted all comments and replies on the aforementioned YouTube clips 1 month after the docuseries' release (N=532). Research assistants manually double-coded the comments using a systematically developed codebook that assessed for NTP sentiment (pro-NTP, anti-NTP, complex sentiment, or no sentiment) and the presence or absence of specific electronic cigarette (e-cigarette)-related content. Given the substantial amount of comments coded as potential misinformation during the coding process, we conducted an in-depth qualitative content analysis of all comments coded as potential misinformation. Simultaneously, we used word clustering techniques including structural topic modeling to identify the overarching topics. Results: Of the 73.8% ( 393/532) relevant comments, 63.6% (250/393) expressed NTP sentiment with 42.8% of these (107/250) expressing pro-NTP sentiment and 18.4% (46/250) expressing complex sentiment. The most frequent content category was potential misinformation (27.5%, 108/393). These 108 comments contained 152 individual pieces of misinformation that were broadly grouped within 6 themes with various numbers of subthemes; the most frequent misinformation theme was that e-cigarette use is completely safe or much safer than smoking (n=80). Other frequently occurring content categories included e-cigarette use is safer than smoking (17.6%, 69/393), and personal experience using e-cigarettes or JUUL (15.5%, 61/393). For topic modeling, we identified 9 topics that we qualitatively assigned into 4 thematic categories: comparisons with other drugs, mentions of government and pharma companies, role of media and parents, and harms associated with nicotine and tobacco products. Conclusions: To the best of our knowledge, this is the first study to examine viewer reactions to the docuseries about JUUL. Our analysis of YouTube comments offers insight into current sentiment and misinformation regarding NTPs and highlights the potential utility of using mixed methods to analyze NTP-related social media data, and the benefits of integrating computational and human qualitative research to analyze social media perceptions of e-cigarettes. Public health professionals can use our findings to help develop tailored health communication messages to address common sentiment and misconceptions related to JUUL, other e-cigarette products, and new NTP products.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".