  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.
