Exploratory Analysis of Electronic Cigarette–Related Videos on YouTube: Observational Study
Bibliographic record
Abstract
BACKGROUND: Electronic cigarette (e-cigarette) use has become more popular than cigarette smoking, especially among youth. Social media platforms, including YouTube, are a popular means of sharing information about e-cigarette use (vaping). OBJECTIVE: This study aimed to characterize the content and user engagement of e-cigarette-related YouTube videos. METHODS: The top 400 YouTube search videos related to e-cigarettes were collected in January 2020. Among them, 340 valid videos were classified into provaping, vaping-warning, and neutral categories by hand coding. Additionally, the content of e-cigarette videos and their user engagement (including average views and likes) were analyzed and compared. RESULTS: While provaping videos were dominant among e-cigarette-related YouTube videos from 2007 to 2017, vaping-warning videos started to emerge in 2013 and became dominant between 2018 and 2019. Compared to vaping-warning videos, provaping videos had higher average daily views (1077 vs 822) but lower average daily likes (12 vs 15). Among 161 provaping videos, videos on user demonstration (n=100, 62.11%) were dominant, and videos on comparison with smoking had the highest user engagement (2522 average daily views and 28 average daily likes). Conversely, among 141 vaping-warning videos, videos on potential health risks were the most popular topic (n=57, 40.42%) with the highest user engagement (1609 average daily views and 33 average daily likes). CONCLUSIONS: YouTube was dominated by provaping videos, with the majority of videos on user demonstrations before 2018. The vaping-warning videos became dominant between 2018 and 2019, with videos on potential health risks being the most popular topic. This study provides updated surveillance on e-cigarette-related YouTube videos and some important guidance on associated social media regulations.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".