  Growing evidence suggests that YouTube's recommendation algorithm plays a
role in online radicalization via surfacing extreme content. Radical Islamist
groups, in particular, have been profiting from the global appeal of YouTube to
disseminate hate and jihadist propaganda. In this quantitative, data-driven
study, we investigate the prevalence of religiously intolerant Arabic YouTube
videos, the tendency of the platform to recommend such videos, and how these
recommendations are affected by demographics and watch history. Based on our
deep learning classifier developed to detect hateful videos and a large-scale
dataset of over 350K videos, we find that Arabic videos targeting religious
minorities are particularly prevalent in search results (30%) and first-level
recommendations (21%), and that 15% of overall captured recommendations point
to hateful videos. Our personalized audit experiments suggest that gender and
religious identity can substantially affect the extent of exposure to hateful
content. Our results contribute vital insights into the phenomenon of online
radicalization and facilitate curbing online harmful content.
