Pushkinskaya st. 43. office 10
Rostov-on-Don, Russia
344082
e-mail: info@hjournal.ru 
tel. +7(863) 269-88-14

cubsEN (2)

Video Content Popularity Prediction Using Machine Learning Methods

Video Content Popularity Prediction Using Machine Learning Methods

Journal of Economic Regulation, , Vol. 11 (no. 2),

This paper deals with the research problem of predicting the popularity of newly created video content. Machine learning task is represented by binary classification of videos into “popular” and “unpopular”. Based on the Pareto principle, the “popular” videos are those, which are part of the top 20% most viewed videos. The article provides an overview of studies on the video content popularity prediction problem by using methods of machine learning (including deep learning). The author explores the applicability of various modifications of existing methods to solve the research problem. The author also develops the new method based on a combination of the ensemble of trees and neural networks. Each method is tested on a sample of 11,000 YouTube videos data, which is collected by using a purposefully developed parsing software. Based on the tests results, it is suggested to use the method of combining tree ensembles and neural networks. The quality of prediction by using this method is characterized by the following metrics: 87% of videos are correctly classified (Accuracy); among the videos classified as popular, 63% are popular (Precision); 49% of truly popular videos are correctly identified (Recall). Research findings indicate characteristics that are most likely to influence the popularity of the newly created video: the number of views and dislikes of the last publi shed video on this channel; the number of channel subscribers; last video's publishing time; new video title; the channel establishment date. The limitations and directions for improving the method are outlined; the need for interdisciplinary research is proposed as encompassing the interests of marketers, data analysts, linguists and psychologists.


Keywords: digital economy; marketing; management; Big data; video content; machine learning methods

References:
Publisher: Ltd. "Humanitarian perspectives"
Founder: Ltd. "Humanitarian perspectives"
Online ISSN: 2412-6047
ISSN: 2078-5429