A COMPARISON OF MACHINE LEARNING ALGORITHMS FOR INDIAN YOUTUBE SPAM FILTER
Asif Hasan, Masood Anzar · Journal of Research in Engineering and Applied Sciences · 2021
Expressing your view on social media has become part of our daily life and one of the most used media is the YouTube comment section, where the user can critics any video published by the creator.With the incessant augmentation in the users of YouTube, the quantity of spam comment has also rose meteorically.In this paper, we propose and developed a comment spam filter, it was achieved by using a supervised learning approach which was trained on our self-generated dataset.In addition to this, literature review of different method used by researcher to identify the spam content is also presented.Furthermore, paper illustrates the description and evaluation of accuracy, precision, and recall of different Machine Learning Technique performance such as Linear classifier, Random forest classifier, AdaBoost classifier, Decision Tree, Naive Bayes, and SVM.