What Factors Shape Whether Digital Food Marketing Appeals to Children?
Bibliographic record
Abstract
Children are exposed to large amounts of unhealthy food marketing on digital media. This marketing contains elements that marketing experts consider child-appealing, such as cartoons, bold colors, and childish font styles. Although these elements capture children’s attention, there is still ambiguity regarding which additional factors also play a role. We aimed to examine the effects of child characteristics (sociodemographic, behavioural, and dietary intake factors) and marketing instance features on whether digital food marketing instances appealed to children. Thirty-nine children from Calgary, Alberta, Canada indicated whether 1660 digital food marketing instances (∼130 per child) appealed to them (‘Is this ad for kids like you’; yes vs. no). Each instance was evaluated by three children. Information on children’s sociodemographic characteristics, screen-related behaviours, and dietary intake was also collected. Agreement between children was measured using the Fleiss’ kappa statistic. Text, labels, objects, and logos extracted from the food marketing instances were combined with the variables collected from the children to fit logistic regression, random forest, conditional inference tree, and gradient boosting models to assess which variables were the most important determinants of child appeal. Agreement between children was low, with an average Fleiss’ kappa of –0.01 (95% CI: –0.09, 0.08). The models indicated that the text contained in the food marketing instances was the most important determinant of child appeal. Besides text, the three most important predictors of child appeal were the household’s highest level of education, children’s vegetable consumption and BMI. The conditional inference tree indicated that children with low consumption of vegetables, high consumption of unhealthy snacks and more time spent using screen devices tended to consider more food marketing instances as child appealing. There is substantial variability among children in which marketing instances appeal to them; even children with similar characteristics disagreed. However, children with poorer dietary intakes reported that more food marketing instances appealed to them. Canadian Institutes of Health Research.
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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 | 0.003 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".