TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to their Source Communities

TL;DR

TUBERAIDER is a system that detects and attributes coordinated hate raids on YouTube videos to their source communities with over 75% accuracy, aiding moderation efforts by understanding attack origins.

Contribution

The paper introduces TUBERAIDER, a novel machine learning approach that attributes hate raids to source communities using comment activity peaks and community language analysis.

Findings

Achieves over 75% accuracy in attribution

Effectively detects attack peaks via comment activity

Successfully applied to real-world data and case studies

Abstract

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.