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Each instance starts from segments submitted to SponsorBlock, then pairs the segment with transcript, video and channel information, and the linked sales/service page. Systems predict three tasks: offer type (ST1), product categories (ST2), and legal-risk flags (ST3). Evidence is evaluated across four cumulative access levels. Monitoring based only on YouTube labels misses many candidates because 45.5% failed disclosure; GPT-5.4 labels were reviewed and iterated, with GPT-5.6-luna independently labeling the development set.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/childsafeads-shared-task-2026-commercial-content-in-child-facing-youtube-videos/273624/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/childsafeads-shared-task-2026-commercial-content-in-child-facing-youtube-videos/273624.png","ImageObject",300,407,{"name":92,"@type":93},"Theodore","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the CHILDSAFEADS shared task about?","Question",{"text":113,"@type":114},"It focuses on commercial content in YouTube videos that are likely to reach children and teenagers, turning legal and disclosure questions into machine-predictable classification tasks.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How are candidate segments for the dataset identified?",{"text":118,"@type":114},"Each instance begins with a SponsorBlock segment, where contributors mark sponsorship start and end points and the extension skips accepted segments, providing an independent signal of possible commercial content.",{"name":120,"@type":111,"acceptedAnswer":121},"What tasks do systems need to predict (ST1, ST2, ST3)?",{"text":122,"@type":114},"ST1 predicts what offer the consumer receives, ST2 assigns product categories, and ST3 identifies legal-risk flags related to disclosure, misleading content, and product restrictions.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},273624,1789472306,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":46,"preview_page_count":4},7971461740886,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","ChildSafeAds Shared Task 2026: Commercial Content in Child-Facing  \nYouTube Videos  \nThales Bertaglia1 * Catalina Goanta1 Gerasimos Spanakis2 Gunes Acar3  \n1Utrecht University, Netherlands  \n2Maastricht University, Netherlands  \n3Radboud University, Netherlands  \narXiv : 2608 . 19 165v 1 [cs .CL] 19 Aug 2026  \nAbstract  \nCHILDSAFEADS1 is a shared task on commercial content in YouTube videos likely to reach children and teenagers. It contains 3,360 videos from 939 channels. Each instance begins with a segment submitted to SponsorBlock, an opensource crowdsourced browser extension whose users mark sponsor segments so that others can skip them. We pair the segment with its available transcript, video and channel information, and a sales or service page linked from the video description. Systems determine what kind of offer is being promoted (ST1), assign product categories (ST2), and identify legal risk flags (ST3) . The evidence is divided into four cumulative access levels, from the transcript to the linked page, so results can be compared against the cost of collecting the data. 45.5% of videos in our data failed to properly use the in-platform ad disclosure method (the“Includes paid promotion” label) . GPT-5.4 produced the labels after the expert organiser team reviewed samples and iterated on the taxonomy, prompts and model choices. GPT-5.6-luna independently labelled the development set. This report describes the task, data and evaluation. An updated version will add participating systems and shared-task results.  \n1 Introduction  \nAdvertising on YouTube often appears inside the video itself, delivered by the same creator and in the same style as the surrounding content. Children and teenagers can struggle to recognise this form of commercial persuasion (De Veirman et al., 2019 ; Loose et al., 2023) . Disclosure is also inconsistent across countries and platforms (Bertaglia et al., 2025 ; Gui et al., 2025b) . An audit of affiliatesponsored YouTube videos found a disclosure in about 10% of cases (Mathur et al., 2018) . Authori-  \n* Corresponding author: [t.f.costabertaglia@uu.nl](t.f.costabertaglia@uu.nl)  \n1The competition is available on CodaBench [https://](https://)[ ](https://)[www.codabench.org/competitions/17595/](www.codabench.org/competitions/17595/)  \nties seeking to monitor this content at scale cannot begin with disclosures alone.  \nSponsorBlock provides candidates independent of platform disclosure. It is an open-source crowdsourced browser extension and public API for skipping sponsor segments in YouTube videos.2 A contributor marks where a sponsorship starts and ends; other users can vote on the submission, and the extension skips accepted segments automatically. The resulting public database provides an independent signal that a video may contain commercial content. 45.5% of videos in our data failed to properly use the in-platform ad disclosure method (the“Includes paid promotion” label) . Therefore, monitoring systems relying only on YouTube’s label would miss many relevant candidates.  \nOnce a candidate segment has been found, the practical questions are straightforward. What is being promoted? What type of product is it? What aspects of the promotion need closer legal review? CHILDSAFEADS turns these questions into three linked classification tasks. Systems can work from the transcript alone or add video metadata, channel information and the linked promotional page. Every submission records which evidence it used. The shared task can therefore compare accuracy at different levels of data access and cost.  \nThis report introduces the 3,360-instance dataset, its splits, the three tasks, the legal taxonomy and the evaluation setup. The updated version will describe the submitted systems and final results.  \n2 Legal Context  \nThe task draws inspiration from parts of EU consumer and audiovisual advertising law to develop labels that can be predicted from the released data. ST1 describes what the consumer recei","cbCaim8umejcxMY5","https://ap.wps.com/l/cbCaim8umejcxMY5","pdf",126699,"English","# Introduction\n## Data Source and Candidate Selection\n## Task Overview and Evidence Levels\n# Legal Context\n## ST1 Consumer Receive Type\n## ST3 Legal Risk Flagging\n### Disclosure Flags\n### Content Flags\n### Product Flags","[{\"question\":\"What is the CHILDSAFEADS shared task about?\",\"answer\":\"It focuses on commercial content in YouTube videos that are likely to reach children and teenagers, turning legal and disclosure questions into machine-predictable classification tasks.\"},{\"question\":\"How are candidate segments for the dataset identified?\",\"answer\":\"Each instance begins with a SponsorBlock segment, where contributors mark sponsorship start and end points and the extension skips accepted segments, providing an independent signal of possible commercial content.\"},{\"question\":\"What tasks do systems need to predict (ST1, ST2, ST3)?\",\"answer\":\"ST1 predicts what offer the consumer receives, ST2 assigns product categories, and ST3 identifies legal-risk flags related to disclosure, misleading content, and product restrictions.\"}]","ChildSafeAds Shared Task 2026 - Commercial Content in Child-Facing YouTube Videos | PDF"]