As of 2018, YouTube, the major online video sharing website, hosts multiple channels promoting right-wing content. In this paper, we observe issues related to hate, violence and discriminatory bias in a dataset containing more than 7,000 videos and 17 million comments. We investigate similarities and differences between users' comments and video content in a selection of right-wing channels and compare it to a baseline set using a three-layered approach, in which we analyze (a) lexicon, (b) topics and (c) implicit biases present in the texts. Among other results, our analyses show that right-wing channels tend to (a) contain a higher degree of words from "negative" semantic fields, (b) raise more topics related to war and terrorism, and (c) demonstrate more discriminatory bias against Muslims (in videos) and towards LGBT people (in comments). Our findings shed light not only into the collective conduct of the YouTube community promoting and consuming right-wing content, but also into the general behavior of YouTube users.
@article{arxiv.1804.04096,
title = {Analyzing Right-wing YouTube Channels: Hate, Violence and Discrimination},
author = {Raphael Ottoni and Evandro Cunha and Gabriel Magno and Pedro Bernadina and Wagner Meira and Virgilio Almeida},
journal= {arXiv preprint arXiv:1804.04096},
year = {2018}
}
Comments
In Proceedings of the 10th ACM Conference on Web Science