  Alas, coordinated hate attacks, or raids, are becoming increasingly common
online. In a nutshell, these are perpetrated by a group of aggressors who
organize and coordinate operations on a platform (e.g., 4chan) to target
victims on another community (e.g., YouTube). In this paper, we focus on
attributing raids to their source community, paving the way for moderation
approaches that take the context (and potentially the motivation) of an attack
into consideration. We present TUBERAIDER, an attribution system achieving over
75% accuracy in detecting and attributing coordinated hate attacks on YouTube
videos. We instantiate it using links to YouTube videos shared on 4chan's /pol/
board, r/The_Donald, and 16 Incels-related subreddits. We use a peak detector
to identify a rise in the comment activity of a YouTube video, which signals
that an attack may be occurring. We then train a machine learning classifier
based on the community language (i.e., TF-IDF scores of relevant keywords) to
perform the attribution. We test TUBERAIDER in the wild and present a few case
studies of actual aggression attacks identified by it to showcase its
effectiveness.
