Food marketing on digital platforms: what do teens see?
Bibliographic record
Abstract
OBJECTIVE: Given the aggressive marketing of foods and beverages to teenagers on digital platforms, and the paucity of research documenting teen engagement with food marketing and its persuasive content, the objective of this study is to examine what teenagers see as teen-targeted food marketing on four popular digital platforms and to provide insight into the persuasive power of that marketing. DESIGN: This is an exploratory, participatory research study, in which teenagers used a special mobile app to capture all teen-targeted food and beverage marketing they saw on digital media for 7 d. For each ad, participants identified the brand, product and specific appeals that made it teen-targeted, as well as the platform on which it was found. SETTING: Online (digital media) with teenagers in Canada. PARTICIPANTS: Two hundred and seventy-eight teenagers, aged 13-17 years, were participated. Most participants were girls (63 %) and older teenagers (58 % aged 16-17 years). RESULTS: Participants captured 1392 teen-targeted food advertisements from Instagram, Snapchat, TikTok and YouTube. The greatest number of food marketing examples came from Instagram (46 %) (with no difference across genders or age), while beverages (28·7 %), fast food (25·1 %) and candy/chocolate were the top categories advertised. When it comes to persuasive power, visual style was the top choice across all platforms and participants, with other top techniques (special offer, theme and humour), ranking differently, depending on age, gender and platform. CONCLUSIONS: This study provides insight into the nature of digital food marketing and its persuasive power for teenagers, highlighting considerations of selection and salience when it comes to examining food marketing and monitoring.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".