Children and adolescents' exposure to food and beverage marketing in social media apps
Bibliographic record
Abstract
BACKGROUND: Unhealthy food marketing is a powerful determinant of unhealthy diets and obesity among children. Although it is known that food marketers target young people on social media, no study has yet quantified children's exposure on these platforms. OBJECTIVES: To compare the frequency and healthfulness of food marketing seen by children and adolescents on social media apps as well as estimate their weekly exposure. METHODS: 101 children and adolescents (ages 7-16 years) completed a survey on their media use and were recorded using their two favourite social media apps for 5 minutes each on the mobile device they usually use. Recordings of app use were reviewed to identify food marketing exposures. RESULTS: Overall, 72% of participants were exposed to food marketing. Of the 215 food marketing exposures identified, most promoted unhealthy products such as fast food (44%) and sugar-sweetened beverages (9%). Adolescents viewed more instances of food marketing, on average, per 10-minute period compared with children (Mean [SD] = 2.6 [2.7] versus 1.4 [2.1], U = 1606, z = 2.94, P = 0.003). It was also estimated that children and adolescents see food marketing 30 and 189 times on average per week on social media apps, respectively. CONCLUSIONS: Statutory regulations restricting unhealthy food marketing to adolescents and children on social media should be considered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".