TY - GEN
T1 - Sexism in Focus: An Annotated Dataset of YouTube Comments for Gender Bias Research
AU - Bertaglia, Thales
AU - Bartekova, Katarina
AU - Jongma, Rinske
AU - Mccarthy, Stephen
AU - Iamnitchi, Adriana
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2023/9/4
Y1 - 2023/9/4
N2 - This paper presents a novel dataset of 200k YouTube comments from 468 videos across 109 channels in four content categories: Entertainment, Gaming, People & Blogs, and Science & Technology. We applied state-of-the-art NLP methods to augment the dataset with sexism-related features such as sentiment, toxicity, offensiveness, and hate speech. These features can assist manual content analyses and enable automated analysis of sexism in online platforms. Furthermore, we develop an annotation framework inspired by the Ambivalent Sexism Theory to promote a nuanced understanding of how comments relate to the gender of content creators. We release a small sample of comments annotated using this framework. Our dataset analysis confirms that female content creators receive more sexist and hateful comments than their male counterparts, underscoring the need for further research and intervention in addressing online sexism.
AB - This paper presents a novel dataset of 200k YouTube comments from 468 videos across 109 channels in four content categories: Entertainment, Gaming, People & Blogs, and Science & Technology. We applied state-of-the-art NLP methods to augment the dataset with sexism-related features such as sentiment, toxicity, offensiveness, and hate speech. These features can assist manual content analyses and enable automated analysis of sexism in online platforms. Furthermore, we develop an annotation framework inspired by the Ambivalent Sexism Theory to promote a nuanced understanding of how comments relate to the gender of content creators. We release a small sample of comments annotated using this framework. Our dataset analysis confirms that female content creators receive more sexist and hateful comments than their male counterparts, underscoring the need for further research and intervention in addressing online sexism.
U2 - 10.1145/3599696.3612900
DO - 10.1145/3599696.3612900
M3 - Conference article in proceeding
SP - 22
EP - 28
BT - OASIS '23: Proceedings of the 3rd International Workshop on Open Challenges in Online Social Networks
ER -