Deradicalizing YouTube: Characterization, Detection, and Personalization of Religiously Intolerant Arabic Videos

TL;DR

This study analyzes the prevalence and recommendation patterns of religiously intolerant Arabic YouTube videos, revealing significant exposure risks influenced by demographics, and introduces a deep learning classifier for hate detection.

Contribution

It provides a large-scale quantitative analysis of radical content on YouTube and develops a deep learning tool for detecting hateful videos, highlighting personalization effects.

Findings

Arabic hate videos are prevalent in search and recommendations.

Gender and religious identity influence exposure to hateful content.

15% of recommendations lead to hateful videos.

Abstract

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.