Hate, Obscenity, and Insults: Measuring the Exposure of Children to Inappropriate Comments in YouTube
- 주제(키워드) NLP , Online Behavior Analysis , YouTube Comments
- 관리정보기술 faculty
- 등재 SCOPUS
- 발행기관 Association for Computing Machinery, Inc
- 발행년도 2021
- 세부유형 Conference Paper
- 회의명 30th World Wide Web Conference, WWW 2021
- 일자 19 April 2021 through 23 April 2021
- URI http://www.dcollection.net/handler/ewha/000000182093
- ISBN 9781450383134
- 본문언어 영어
- Published As http://dx.doi.org/10.1145/3442442.3452314
초록/요약
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. © 2021 ACM.
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