Detecting Suspicious Commenter Mob Behaviors on YouTube Using Graph2Vec
Shadi Shajari Mustafa Alassad Nitin Agarwal
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.