Tobacco on the web: surveillance and characterisation of online tobacco and e-cigarette advertising
Bibliographic record
Abstract
BACKGROUND: Despite the internet's broad reach and potential to influence consumer behaviour, there has been little examination of the volume, characteristics, and target audience of online tobacco and e-cigarette advertisements. METHODS: A full-service advertising firm was used to collect all online banner/video advertisements occurring in the USA and Canada between 1 April 2012 and 1 April 2013. The advertisement and associated meta-data on brand, date range observed, first market, and spend were downloaded and summarised. Characteristics and themes of advertisements, as well as topic area and target demographics of websites on which advertisements appeared, were also examined. RESULTS: Over a 1-year period, almost $2 million were spent by the e-cigarette and tobacco industries on the placement of their online product advertisements in the USA and Canada. Most was spent promoting two brands: NJOY e-cigarettes and Swedish Snus. There was almost no advertising of cigarettes. About 30% of all advertisements mentioned a price promotion, discount coupon or price break. e-Cigarette advertisements were most likely to feature messages of harm reduction (38%) or use for cessation (21%). Certain brands advertised on websites that contained up to 35% of youth (<18 years) as their audience. CONCLUSIONS: Online banner/video advertising is a tactic used mainly to advertise e-cigarettes and cigars rather than cigarettes, some with unproven claims about benefits to health. Given the reach and accessibility of online advertising to vulnerable populations such as youth and the potential for health claims to be misinterpreted, online advertisements need to be closely monitored.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".