Content Analysis of Trends in Print Magazine Tobacco Advertisements
Bibliographic record
Abstract
OBJECTIVES: To provide a descriptive and comparative content analysis of tobacco print magazine ads, with a focus on rhetorical and persuasive themes. METHODS: Print tobacco ads for cigarettes, cigars, e-cigarettes, moist snuff, and snus (N = 171) were content analyzed for the physical composition/ad format (e.g., size of ad, image, setting, branding, warning label) and the content of the ad (e.g., rhetorical themes, persuasive themes). RESULTS: The theme of pathos (that elicits an emotional response) was most frequently utilized for cigarette (61%), cigar (50%), and moist snuff (50%) ads, and the theme of logos (use of logic or facts to support position) was most frequently used for e-cigarette (85%) ads. Additionally, comparative claims were most frequently used for snus (e.g., "spit-free," "smoke-free") and e-cigarette ads (e.g., "no tobacco smoke, only vapor," "no odor, no ash"). Comparative claims were also used in cigarette ads, primarily to highlight availability in different flavors (e.g., "bold," "menthol"). CONCLUSIONS: This study has implications for tobacco product marketing regulation, particularly around limiting tobacco advertising in publications with a large youth readership and prohibiting false or misleading labels, labeling, and advertising for tobacco products, such as modified risk (unless approved by the FDA) or therapeutic claims.
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.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".