Food marketing to teenagers: Examining the digital palate of targeted appeals
Bibliographic record
Abstract
Food marketing is a conspicuous part of the digital landscape for teenagers, with the aims of influencing preferences, purchases and consumption. Yet little is known about the nature and persuasive power of such marketing, especially across the platforms most popular with teens. Given this research gap, this exploratory study aimsed to shed light on the "digital palate" being advertised to teenagers and the specific appeals they found salient within that advertising. Teenagers (ages 13-17, n = 468) engaged in participatory research, capturing the teen-targeted food advertising that they encountered over the span of one week. For each ad, they identified the product, brand, platform and specific techniques they felt made the ad teen-targeted. Results reveal the pervasive and expansive nature of teen-targeted food marketing: 3385 advertisements were collected from 557 distinct food and beverage brands from the digital platforms of Instagram, TikTok, Snapchat, and YouTube. Instagram trumped all other platforms when it came to food marketing, but the "digital palate" promoted across all platforms was consistent. Beverages, fast food and candy/chocolate comprised the top categories of foods advertised to teens. Ads for these generally unhealthy (and sweet) products were considered persuasive due to their visual style and special offers-ones that focused on convenience, novelty, bold flavors, limited edition products and (even more) digital engagement. While the digital palate promoted was salient and engaging to teenagers, the food (and food categories) promoted do not work to support long term health.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".