# Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children

arXiv: https://arxiv.org/abs/1901.07046 · HTML: https://mlsanity.com/paper/1901.07046/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2019.

## Built on by (most cited first, 13 of 13)

- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models](https://mlsanity.com/paper/2403.18957/) (2024)
- [Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways](https://mlsanity.com/paper/2202.00640/) (2022)
- [Malicious or Benign? Towards Effective Content Moderation for Children's Videos](https://mlsanity.com/paper/2305.15551/) (2023)
- [Hate, Obscenity, and Insults: Measuring the Exposure of Children to Inappropriate Comments in YouTube](https://mlsanity.com/paper/2103.09050/) (2021)
- [VidHarm: A Clip Based Dataset for Harmful Content Detection](https://mlsanity.com/paper/2106.08323/) (2021)
- [Enhanced Multimodal Content Moderation of Children's Videos using Audiovisual Fusion](https://mlsanity.com/paper/2405.06128/) (2024)
- ['Beach' to 'Bitch': Inadvertent Unsafe Transcription of Kids' Content on YouTube](https://mlsanity.com/paper/2203.04837/) (2022)
- [SNIFR : Boosting Fine-Grained Child Harmful Content Detection Through Audio-Visual Alignment with Cascaded Cross-Transformer](https://mlsanity.com/paper/2506.03378/) (2025)
- [Privacy-Preserving Online Content Moderation: A Federated Learning Use Case](https://mlsanity.com/paper/2209.11843/) (2022)
- [Are Open-Weight LLMs Ready for Social Media Moderation? A Comparative Study on Bluesky](https://mlsanity.com/paper/2602.05189/) (2026)
- [Auditing Engagement Incentives in the Kidfluencer Ecosystem: A Multimodal Weak Supervision Approach](https://mlsanity.com/paper/2606.03173/) (2026)
- [N"urnberg NLP at ChildSafeAds 2026: Structurally Dissimilar Voter Ensembles under Four Levels of Data Access](https://mlsanity.com/paper/2609.34986/) (2026)

## Abstract (arXiv)

A large number of the most-subscribed YouTube channels target children of a very young age. Hundreds of toddler-oriented channels on YouTube feature inoffensive, well-produced, and educational videos. Unfortunately, inappropriate content that targets this demographic is also common. YouTube's algorithmic recommendation system regrettably suggests inappropriate content because some of it mimics or is derived from otherwise appropriate content. Considering the risk for early childhood development, and an increasing trend in toddler's consumption of YouTube media, this is a worrisome problem. In this work, we build a classifier able to discern inappropriate content that targets toddlers on YouTube with 84.3% accuracy, and leverage it to perform a first-of-its-kind, large-scale, quantitative characterization that reveals some of the risks of YouTube media consumption by young children. Our analysis reveals that YouTube is still plagued by such disturbing videos and its currently deployed counter-measures are ineffective in terms of detecting them in a timely manner. Alarmingly, using our classifier we show that young children are not only able, but likely to encounter disturbing videos when they randomly browse the platform starting from benign videos.