# Hate, Obscenity, and Insults: Measuring the Exposure of Children to Inappropriate Comments in YouTube

arXiv: https://arxiv.org/abs/2103.09050 · HTML: https://mlsanity.com/paper/2103.09050/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017)
- [Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children](https://mlsanity.com/paper/1901.07046/) (2019)
- [Measuring and Characterizing Hate Speech on News Websites](https://mlsanity.com/paper/2005.07926/) (2020)
- ["You Know What to Do": Proactive Detection of YouTube Videos Targeted by Coordinated Hate Attacks](https://mlsanity.com/paper/1805.08168/) (2018)
- [KidsTube: Detection, Characterization and Analysis of Child Unsafe Content & Promoters on YouTube](https://mlsanity.com/paper/1608.05966/) (2016)
- [Hiding in Plain Sight: A Measurement and Analysis of Kids' Exposure to Malicious URLs on YouTube](https://mlsanity.com/paper/2009.07923/) (2020)

## Built on by (most cited first, 4 of 4)

- [Malicious or Benign? Towards Effective Content Moderation for Children's Videos](https://mlsanity.com/paper/2305.15551/) (2023)
- [The Potential of Vision-Language Models for Content Moderation of Children's Videos](https://mlsanity.com/paper/2312.03936/) (2023)
- [A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in English](https://mlsanity.com/paper/2504.08609/) (2025)
- [Privacy-Preserving Online Content Moderation: A Federated Learning Use Case](https://mlsanity.com/paper/2209.11843/) (2022)

## Abstract (arXiv)

Social media has become an essential part of the daily routines of children and adolescents. Moreover, enormous efforts have been made to ensure the psychological and emotional well-being of young users as well as their safety when interacting with various social media platforms. In this paper, we investigate the exposure of those users to inappropriate comments posted on YouTube videos targeting this demographic. We collected a large-scale dataset of approximately four million records and studied the presence of five age-inappropriate categories and the amount of exposure to each category. Using natural language processing and machine learning techniques, we constructed ensemble classifiers that achieved high accuracy in detecting inappropriate comments. Our results show a large percentage of worrisome comments with inappropriate content: we found 11% of the comments on children's videos to be toxic, highlighting the importance of monitoring comments, particularly on children's platforms.