Portrayal of electronic cigarettes on YouTube
Bibliographic record
Abstract
BACKGROUND: As the most popular video sharing website in the world, YouTube has the potential to reach and influence a huge audience. This study aims to gain a systematic understanding of what e-cigarette messages people are being exposed to on YouTube by assessing the quantity, portrayal and reach of e-cigarette videos. METHODS: Researchers identified the top 20 search results on YouTube by relevance and view count for the following search terms: "electronic cigarettes", "e-cigarettes", "ecigarettes", "ecigs", "smoking electronic cigarettes", "smoking e-cigarettes", "smoking ecigarettes", "smoking ecigs". A sample of 196 unique videos was coded for overall portrayal and genre. Main topics covered in e-cigarette videos were recorded and video statistics and viewer demographic information were documented. RESULTS: Among the 196 unique videos, 94% (n = 185) were "pro" to e-cigarettes and 4% (n = 8) were neutral, while there were only 2% (n = 3) that were "anti" to e-cigarettes. The top 3 most prevalent genres of videos were advertisement, user sharing and product review. 84.3% of "pro" videos contained Web links for e-cigarette purchase. 71.4% of "pro" videos claimed that e-cigarettes were healthier than conventional cigarettes. Audience was primarily from the United States, the United Kingdom and Canada and "pro" e-cigarette videos were watched more frequently and rated much more favorably than "anti" ones. CONCLUSIONS: The vast majority of information on YouTube about e-cigarettes promoted their use and depicted the use of e-cigarettes as socially acceptable. It is critical to develop appropriate health campaigns to inform e-cigarette consumers of potential harms associated with e-cigarette use.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".