Classifying YouTube Videos Based on Their Quality: A Comparative Study of Seven Machine Learning Algorithms

YouTube is a popular social network for the distribution of videos globally and it is a one of the popular platforms that a large number of people access daily. On the YouTube platform, there are both high-quality videos and low-quality videos. Therefore, it is important to identify high-quality videos among the videos on YouTube to gain a full advantage of them without wasting time and make predictions of the available videos on YouTube. We identified view count, like count, comment count, caption, number of subscribers, tag count, total views, total videos, avg. polarity score, duration secs, title length, and description length as the main factors to decide the video quality. The purpose of this approach is to suggest seven different classification algorithms to make predictions of the quality/integrity of YouTube videos for users. The collected data set was pre-processed as necessary by cleaning the data groups and removing unnecessary attributes by the attribute ranking. This study was conducted using seven classification algorithms Random Forest, Logistic Regression, Support Vector Machine (SVM), Decision Tree, Multilayer Perception (MLP), Naive Bayes, and ensemble learning algorithm that combined the five individual algorithms. The model used 60% training data and 40% testing data for the classification by the Waikato Environment for Knowledge Analysis (WEKA) tool. The accuracy of each algorithm, Precision, Recall, and F-Measure are considered and Random Forest is identified as the best individual algorithm with 96.89% testing accuracy and ensemble learning with 97.21% testing accuracy. Based on the results, this model enables YouTube users to identify the quality/integrity of YouTube videos and take their decisions to view those videos.