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

Sultan Alshamrani, Ahmed Abusnaina, Mohammed Abuhamad, Daehun Nyang, David Mohaisen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationThe Web Conference 2021 - Companion of the World Wide Web Conference, WWW 2021
PublisherAssociation for Computing Machinery, Inc
Pages508-515
Number of pages8
ISBN (Electronic)9781450383134
DOIs
StatePublished - 19 Apr 2021
Event30th World Wide Web Conference, WWW 2021 - Ljubljana, Slovenia
Duration: 19 Apr 202123 Apr 2021

Publication series

NameThe Web Conference 2021 - Companion of the World Wide Web Conference, WWW 2021

Conference

Conference30th World Wide Web Conference, WWW 2021
Country/TerritorySlovenia
CityLjubljana
Period19/04/2123/04/21

Keywords

  • NLP
  • Online Behavior Analysis
  • YouTube Comments

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