Understanding Targeted Video-Ads in Children's Content
Bibliographic record
Abstract
As the volume of online video entertainment via streaming increases, ever so more are users targeted by online advertisement algorithms. Nevertheless, this rise in targeting and revenue does not come without any concerns. That is, even though the online advertising business model has is very successful, nowadays, rising societal concerns regarding the ethics and extent to which such algorithms agree with the laws of different countries are also present. Motivated by the dichotomy above, we here explore how targeted video-ads meet the regulatory policies regarding children advertising in Brazil and Canada. To perform our study, we create synthetic user personas that watch YouTube videos daily. Our personas are tailored to stream children's content while controlling for several variables (e.g., gender, country, and type of content streamed). With the data gathered, our analyses reveal statistical evidence of algorithmic targeting in videos geared towards children. Also, some of the advertised products (e.g., alcoholic beverages and fast-food) go directly against the regulations of the studied countries. With advertisements being matched to users by machine learning algorithms, it is impossible to state whether regulations are not followed on purpose (e.g., advertisers gaming the system). Nevertheless, our findings and discussion do raise a flag that regulations may not be sufficient, and content providers may still need to audit systems to meet the regulations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".