#notforkids: alcohol, vaping, and cannabis marketing by social media influencers popular with children and adolescents on YouTube, Instagram, and TikTok and policy implications
Bibliographic record
Abstract
BACKGROUND: Despite the popularity of social media among children and adolescents, there is comparatively little research on social media influencer marketing, particularly in the context of harmful products. The purpose of this study was to examine the frequency of advertisements for alcohol, vaping, and cannabis products/brands promoted by social media influencers popular with Canadian children and adolescents on YouTube, Instagram, and TikTok and analyze the marketing techniques used. METHODS: The top 9 influencers among Canadian children (10-12 years) and top 8 among Canadian adolescents (13-17 years) were identified from the 2021 International Food Policy Study. A subset of posts on YouTube, Instagram, and TikTok between June 1, 2021, and May 31, 2022, were examined for alcohol, vaping, and cannabis marketing. The frequency of marketing instances for each commodity was determined by age group and platform. RESULTS: We found no cannabis or vaping marketing. Influencers popular with children made 25 posts with alcohol marketing on Instagram and YouTube, showcasing 34 products/brands, while influencers popular with adolescents made 9 posts with alcohol marketing, featuring 16 alcohol products/brands. TikTok posts did not feature any alcohol products. Among influencers popular with children, YouTube accounted for most alcohol-related posts (72%), with beer being the most promoted (47%). Among influencers popular with adolescents, posts were mostly on Instagram (78%), with spirits being the most promoted (75%). Most posts across both age groups showed the product. Songs/music and appeals to fun/cool were the most common marketing techniques among influencers popular with children and adolescents, respectively. CONCLUSIONS: Alcohol marketing that is appealing to children and adolescents is restricted in Canada, though they are likely exposed to such marketing ostensibly directed to adults. Further regulation, monitoring, and compliance assessments are warranted.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".