The Advertising Policies of Major Social Media Platforms Overlook the Imperative to Restrict the Exposure of Children and Adolescents to the Promotion of Unhealthy Foods and Beverages
Bibliographic record
Abstract
There have been global calls to action to protect children (aged <18) from exposure to the marketing of unhealthy foods and beverages ('unhealthy foods'). In this context, the rising popularity of social media, particularly amongst adolescents, represents an important focus area. This study aimed to examine the advertising policies of major global social media platforms related to the advertising of unhealthy foods, and to identify opportunities for social media platforms to take action. We conducted a desk-based review of the advertising policies of the 16 largest social media platforms globally. We examined their publicly available advertising policies related to food and obesity, as well as in relation to other areas impacting public health. The advertising policies for 12 of the selected social media platforms were located. None of these platforms adopted comprehensive restrictions on the advertising of unhealthy foods, with only two platforms having relevant (but very limited) policies in the area. In comparison, 11 of the 12 social media platforms had policies restricting the advertising of alcohol, tobacco, gambling, and/or weight loss. There is, therefore, an opportunity for major social media platforms to voluntarily restrict the exposure of children to the marketing of unhealthy foods, which can contribute to efforts to improve populations' diets.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".