mlsanityData to 14 Oct 2026

ChildSafeAds Shared Task 2026: Commercial Content in Child-Facing YouTube Videos

arXiv 2608.19165 ↗First posted 19 Aug 2026cs.CL cs.CYcited by 0

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Abstract (arXiv)

ChildSafeAds is a shared task on commercial content in YouTube videos likely to reach children and teenagers. The dataset contains 3,360 videos from 939 channels, each starting with a sponsor segment submitted to SponsorBlock. We pair each segment with its transcript, video, and channel information, plus a sales or service page linked from the video description. Systems determine the type of offer being promoted (ST1), assign product categories (ST2), and identify potential legal risks (ST3). Evidence is divided into four cumulative access levels, from the transcript to the linked page, so performance can be compared against the cost of collecting additional data. YouTube's "Includes paid promotion" label was absent from 45.5% of the videos. GPT-5.4 produced the labels after the organiser team, including a legal expert, reviewed samples and iterated on the taxonomy, prompts and model choices. GPT-5.6-luna independently labelled the development set to measure cross-model agreement. The evaluation received 21 entries; the highest mean macro-F1 was 0.735, while the best transcript-only entry reached 0.652. Across four system papers, the linked promotional page mainly helped identify what was being sold and added little to compliance-risk classification. The competition also exposed sensitivity to rare labels, advertiser overlap across splits and substantial variation in the ST3 labels.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 26 references that are in the map. First posted on arXiv 19 Aug 2026.

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9 of 26 references.

References · 9

  1. 2021LexGLUE: A Benchmark Dataset for Legal Language Understanding in Englishcited by 310
  2. 2021Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platformcited by 66
  3. 2018An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterestcited by 10
  4. 2023Closing the Loop: Testing ChatGPT to Generate Model Explanations to Improve Human Labelling of Sponsored Content on Social Mediacited by 9
  5. 2024Automated Transparency: A Legal and Empirical Analysis of the Digital Services Act Transparency Databasecited by 7
  6. 2024Across Platforms and Languages: Dutch Influencers and Legal Disclosures on Instagram, YouTube and TikTokcited by 5
  7. 2024LegalLens Shared Task 2024: Legal Violation Identification in Unstructured Textcited by 5
  8. 2024Influencer Self-Disclosure Practices on Instagram: A Multi-Country Longitudinal Studycited by 4
  9. 2025Evaluating LLM-Generated Legal Explanations for Regulatory Compliance in Social Media Influencer Marketingcited by 4

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