Spam detection for Youtube video comments using machine learning approaches

Abstract

Machine Learning models have the ability to streamline the process by which Youtube video comments are filtered between legitimate comments (ham) and spam. In order to integrate machine learning models into regular usage on media-sharing platforms, recent approaches have aimed to develop models trained on Youtube comments, which have emerged as valuable tools for the classification and have enabled the identification of spam content and enhancing user experience. In this paper, eight machine learning approaches are applied to spam detection for YouTube comments. The eight machine learning models include Gaussian Naive Bayes, logistic regression, K-nearest neighbors (KNN) classifier, multi-layer perceptron (MLP), support vector machine (SVM) classifier, random forest classifier, decision tree classifier, and voting classifier. All eight models perform very well, specifically random forest approach can achieve almost perfect performance with average precision of 100% and AUC-ROC of 0.9841. The computational complexity of the eight machine learning approaches are compared.

Keywords

• Machine learning
• Spam detection
• Random forest
• Naive Bayes
• Logistic regression
• Multi-layer perceptron