Tracking teen food marketing: Participatory research to examine persuasive power and platforms of exposure
Bibliographic record
Abstract
Food marketing has long been recognized to influence children's food preferences and consumption patterns, yet only in recent years have teenagers been recognized as a uniquely vulnerable audience for food marketing appeals. Marketing pressures on teenagers around food promotion continue to intensify, yet little is known about the marketing channels and specific persuasive appeals targeting this audience. Given this research gap, this participatory research study engages teenagers to capture the food marketing targeting them and to identify its persuasive "power" and platforms of exposure. Using a specially designed mobile app called GrabFM! (Grab Food Marketing!) teenagers (ages 13-17, n = 309) identified and tagged examples of teen-targeted food marketing in their physical and digital environments over a 7-day period. Results reveal that: 1) digital platforms dominate teen-targeted food marketing, with over three quarters of the ads found on Instagram, Snapchat, TikTok, ad YouTube; 2) branded beverages, fast food, and candy/chocolate comprise the majority (72%) of ads; and 3) the most powerful techniques for attracting teens attention are visual style, special offer and theme. In 40% of advertisements submitted, teenagers used only one indicator to identify "teen-targeted", although older teenagers (ages 15-17) were more likely to report multiple indicators per ad. This study provides important insights into the platforms targeting teenagers (and their relative importance), the food products endorsed, and the specific appeals that teenagers find persuasive. For the purposes of monitoring, it is helpful to know that digital platforms comprise the majority of teen-directed food promotions, and that the Big Food brands have been joined by countless smaller players to sell food to teens.
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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 | 0.001 | 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".