MACHINE LEARNING BASE SPAM COMMENTS DETECTION ON YOUTUBE

Abstract - The proliferation of online video platforms like YouTube has led to a significant increasein user- generated content, including comments. However, this growth has also been accompanied bya surge in spam comments, which can negatively impact user experience, distort discussions, and evenspread malicious links. This project aims to develop an effective spam comment detection systemspecifically tailored for YouTube videos. We investigate various machine learning and naturallanguage processing (NLP) techniques to accurately classify comments as either spam or legitimate.Our methodology involves feature extraction from comment text, including lexical, syntactic, andsemantic features, alongside metadata such as user activity and comment timestamps. We explore theperformance of several classification algorithms, including Support Vector Machines (SVM), RandomForests, and deep learning models like Recurrent Neural Networks (RNNs) and Transformers. Theevaluation of our system is conducted using a custom-built dataset of YouTube comments, focusingon precision, recall, and F1-score metrics. The results demonstrate the feasibility of automated spamcomment detection and highlight the potential for improving user experience on video-sharingplatforms. Keywords: NLP, Feature Extraction , Dataset Creation ,SVM, RNN.