Detecting Suspicious Commenter Mob Behaviors on YouTube Using Graph2Vec
This work addresses the issue of coordinated suspicious behavior in YouTube comments for platform moderators and researchers, but it is incremental as it applies an existing method to a new dataset.
The paper tackled the problem of detecting suspicious commenter mob behaviors on YouTube by analyzing 20 channels with 7,782 videos and 596,982 comments, revealing significant similarities among channels that propagate false views about the U.S. Military.
YouTube, a widely popular online platform, has transformed the dynamics of con-tent consumption and interaction for users worldwide. With its extensive range of content crea-tors and viewers, YouTube serves as a hub for video sharing, entertainment, and information dissemination. However, the exponential growth of users and their active engagement on the platform has raised concerns regarding suspicious commenter behaviors, particularly in the com-ment section. This paper presents a social network analysis-based methodology for detecting suspicious commenter mob-like behaviors among YouTube channels and the similarities therein. The method aims to characterize channels based on the level of such behavior and identify com-mon patterns across them. To evaluate the effectiveness of the proposed model, we conducted an analysis of 20 YouTube channels, consisting of 7,782 videos, 294,199 commenters, and 596,982 comments. These channels were specifically selected for propagating false views about the U.S. Military. The analysis revealed significant similarities among the channels, shedding light on the prevalence of suspicious commenter behavior. By understanding these similarities, we contribute to a better understanding of the dynamics of suspicious behavior on YouTube channels, which can inform strategies for addressing and mitigating such behavior.