Identifying content unaware features influencing popularity of videos on YouTube: A study based on seven regions

Abstract

Predicting the popularity of User Generated Content (UGC) is a subject of interest to the Internet service providers, content makers, social media researchers, and online advertisers. However, it is also a challenging task due to multiple factors that influence social networks' content popularity. This work utilizes the Artificial Intelligence (AI) techniques to identify the features that contribute towards a video to enter into the trending category on YouTube. It examines the data generated by a video and its potential to get trending. For this, the present work utilizes AI for feature prediction. An AI-based methodology is presented that assesses the impact of various content-agnostic factors regarding video popularity in seven different regions, including Canada, France, Germany, India, Pakistan, United Arab Emirates, and the United States of America. A dataset is extracted from YouTube for these regions, and feature selection techniques are executed on the datasets to extract important attributes. A class label is assigned to each video, and the dataset is profiled having one of the two class labels, i.e., trending or non-trending. The top three features for each video (region wise) are obtained. It is observed that the trending behavior is dissimilar in different regions. Finally, three classifiers, namely, artificial neural networks, k-Nearest Neighbor, and support vector machine, are trained to predict if a video can get into the trending category on YouTube. The proposed solution is compared with two closely related state-of-the-art methods. This work is useful for content creators and YouTubers to make their video trending and more appealing.