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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.<\/jats:p>","DOI":"10.1145\/3555618","type":"journal-article","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T22:58:54Z","timestamp":1668207534000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Deradicalizing YouTube: Characterization, Detection, and Personalization of Religiously Intolerant Arabic Videos"],"prefix":"10.1145","volume":"6","author":[{"given":"Nuha","family":"Albadi","sequence":"first","affiliation":[{"name":"Taibah University &amp; University of Colorado, Medina, Saudi Arabia"}]},{"given":"Maram","family":"Kurdi","sequence":"additional","affiliation":[{"name":"Taif University &amp; University of Colorado, Taif, Saudi Arabia"}]},{"given":"Shivakant","family":"Mishra","sequence":"additional","affiliation":[{"name":"University of Colorado, Boulder, CO, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Tensorflow: A system for large-scale machine learning. 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