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Ms. Abha Maurya
Dr. Swapnil Singhal
Keywords:
Social Media, Machine Learning, Spam Detection, YouTube, Algorithms, Digital Data, Artificial Intelligence.
Abstract:
YouTube has become one of the most influential and widely used video-sharing platforms, enabling billions of users to engage through comment-based interactions. However, this open and interactive environment is increasingly exploited by spammers who post malicious links, fraudulent schemes, adult content promotions, and deceptive advertisements. Due to the massive volume of user-generated content, manual moderation is neither scalable nor efficient. Machine Learning (ML) provides an effective and automated solution for detecting spam by analyzing textual content, user behavior, and contextual features of comments. This review paper presents a comprehensive examination of ML-based approaches for YouTube spam comment detection, covering commonly used datasets, feature extraction and selection techniques, evaluation metrics, and state-of-the-art detection frameworks. Furthermore, the paper discusses existing challenges, identifies research gaps, and outlines future directions aimed at developing more robust, adaptive, and scalable spam detection systems.
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International Journal of Recent Research and Review
ISSN: 2277-8322
Vol. XIX, Issue 2
June 2026
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PUBLISHED
June 2026
ISSUE
Vol. XIX, Issue 2
SECTION
Articles
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