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Video sharing platforms like YouTube are increasingly targeted by aggressionand hate attacks. Prior work has shown how these attacks often take place as aresult of "raids," i.e., organized efforts by ad-hoc mobs coordinating fromthird-party communities. Despite the increasing relevance of this phenomenon,however, online services often lack effective countermeasures to mitigate it.Unlike well-studied problems like spam and phishing, coordinated aggressivebehavior both targets and is perpetrated by humans, making defense mechanismsthat look for automated activity unsuitable. Therefore, the de-facto solutionis to reactively rely on user reports and human moderation.  In this paper, we propose an automated solution to identify YouTube videosthat are likely to be targeted by coordinated harassers from fringe communitieslike 4chan. First, we characterize and model YouTube videos along several axes(metadata, audio transcripts, thumbnails) based on a ground truth dataset ofvideos that were targeted by raids. Then, we use an ensemble of classifiers todetermine the likelihood that a video will be raided with very good results(AUC up to 94%). Overall, our work provides an important first step towardsdeploying proactive systems to detect and mitigate coordinated hate attacks onplatforms like YouTube.

"You Know What to Do": Proactive Detection of YouTube Videos Targeted by Coordinated Hate Attacks