Safeguarding youtube discussions

A framework for detecting anomalous commenter and engagement behaviors

Bibliographic Data

ID16859404
AuthorsShadi Shajari (0009-0001-8500-1146, University of Arkansas at Little Rock), Neil Agarwal (0000-0002-5612-4753, University of Arkansas at Little Rock, corresponding author)
Year2025
Volume15
Issue1
Publication date2025-05-28
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSocial Network Analysis and Mining (JOURNAL)
Journal identifiersISSN: 1869-5450 • E-ISSN: 1869-5469
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s13278-025-01470-7
OpenAlexW4410830082
LanguageEN
Citations received1
References cited46

In today’s digital landscape, YouTube’s comment sections play an essential role in shaping public opinion and discussions. However, coordinated groups often exploit these platforms to spread misinformation and distort conversations. This study introduces a method to detect and quantify such anomalous behavior on YouTube channels by analyzing two key components: commenter behaviors and engagement patterns. Focusing on channels concerning news outlets, geopolitics, and the military, we draw from a dataset with 71 channels, 642,952 videos, 12,425,587 commenters, 123,882,200 comments, 139,985,870 subscribers, and 83,396,188,807 views. Our approach leverages key engagement indicators, such as comments, views, and subscriber counts, alongside an analysis of frequent commenter interactions to identify anomalous patterns. We apply unsupervised techniques, including kernel density estimation (KDE) and Gaussian mixture model (GMM), to assign a score reflecting anomalous commenter behavior. To provide a comprehensive measure of this behavior, we combine commenter and engagement scores both at the feature level and at the output level. At the feature level, we employ cosine similarity and principal component analysis (PCA). At the output level, we propose three scoring methods: harmonic mean (HM), weighted average with interaction term (WAIT), and agreement-weighted maximum (AWM). The resulting score, normalized between 0 and 1, indicates the level of anomalous activity on each channel. We validate our results by comparing detected anomalous channels with actual suspended channels from YouTube, confirming the model’s real-world applicability. By offering a quantifiable measure of this activity, our method helps preserve the integrity of YouTube discussions

Data science · Knowledge management · Safeguarding · Computer Science · Hate Speech and Cyberbullying Detection · Medicine · Misinformation and Its Impacts · Sentiment Analysis and Opinion Mining

Tools

Open DOIOpen Access