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.

本研究采用社交网络分析的方法，针对 YouTube 频道中的可疑评论者类似行为进行检测和鉴别，分析了 20 个 YouTube 频道（包括 7782 个视频、294199 名评论者和 596982 条评论）中关于美国军事的虚假观点，并发现了这些频道之间存在显著相似性，从而为揭示和解决 YouTube 频道上的可疑行为提供了更好的理解和应对策略。

利用 Graph2Vec 检测 YouTube 上可疑评论者群体行为

Detecting Suspicious Commenter Mob Behaviors on YouTube Using Graph2Vec

We address the problem of predicting the leading political ideology, i.e.,
left-center-right bias, for YouTube channels of news media. Previous work on
the problem has focused exclusively on text and on analysis of the language
used, topics discussed, sentiment, and the like. In contrast, here we study
videos, which yields an interesting multimodal setup. Starting with gold
annotations about the leading political ideology of major world news media from
Media Bias/Fact Check, we searched on YouTube to find their corresponding
channels, and we downloaded a recent sample of videos from each channel. We
crawled more than 1,000 YouTube hours along with the corresponding subtitles
and metadata, thus producing a new multimodal dataset. We further developed a
multimodal deep-learning architecture for the task. Our analysis shows that the
use of acoustic signal helped to improve bias detection by more than 6%
absolute over using text and metadata only. We release the dataset to the
research community, hoping to help advance the field of multi-modal political
bias detection.

该研究通过构建多模数据集和深度学习算法，探讨了基于视频和音频进行新闻 YouTube 频道的政治意识形态预测问题，并在文本和元数据的基础上通过使用声音信号，将偏见检测能力提高了 6% 以上。