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- W3128100528 abstract "Video sharing platforms like YouTube are increasingly targeted by aggression and hate attacks. Prior workhas shown how these attacks often take place as a result of “raids,” i.e., organized efforts by ad-hoc mobscoordinating from third-party communities. Despite the increasing relevance of this phenomenon, however,online services often lack effective countermeasures to mitigate it. Unlike well-studied problems like spamand phishing, coordinated aggressive behavior both targets and is perpetrated by humans, making defensemechanisms that look for automated activity unsuitable. Therefore, the de-facto solution is to reactively relyon user reports and human moderation.In this paper, we propose an automated solution to identify YouTube videos that are likely to be targeted bycoordinated harassers from fringe communities like 4chan. First, we characterize and model YouTube videosalong several axes (metadata, audio transcripts, thumbnails) based on a ground truth dataset of videos thatwere targeted by raids. Then, we use an ensemble of classifiers to determine the likelihood that a video will beraided 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 on platforms like YouTube." @default.
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- W3128100528 date "2018-11-01" @default.
- W3128100528 modified "2023-09-26" @default.
- W3128100528 title "You Know What to Do: Proactive Detection of YouTube Videos Targeted by Coordinated Hate Attacks" @default.
- W3128100528 hasPublicationYear "2018" @default.
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