Tobacco on the web: surveillance and characterisation of online tobacco and e-cigarette advertising
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it