Illegal loot box advertising on social media? An empirical study using the Meta and TikTok ad transparency repositories
Bibliographic record
Abstract
Loot boxes are gambling-like products inside video games that can be bought with real-world money to obtain random rewards. They are widely available to children, and stakeholders are concerned about potential harms, e.g., overspending. UK advertising must disclose, if relevant, that a game contains (i) any in-game purchases and (ii) loot boxes specifically. An empirical examination of relevant adverts on Meta-owned platforms (i.e., Facebook, Instagram, and Messenger) and TikTok revealed that only about 7% disclosed loot box presence. The vast majority of social media advertising (93%) was therefore non-compliant with UK advertising regulations and also EU consumer protection law. In the UK alone, the 93 most viewed TikTok adverts failing to disclose loot box presence were watched 292,641,000 times total or approximately 11 impressions per active user. Many people have therefore been repeatedly exposed to prohibited and socially irresponsible advertising that failed to provide important and mandated information. Implementation deficiencies with ad repositories, which must comply with transparency obligations imposed by the EU Digital Services Act, are also highlighted, e.g., not disclosing the beneficiary. How data access empowered by law can and should be used by researchers is practically demonstrated. Policymakers should consider enabling more such opportunities for the public benefit.
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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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".