Early viewers or followers: a mathematical model for YouTube viewers' categorization. Issue 6 (3rd August 2020)

• Record Type: Journal Article
• Authors: Aggrawal, Niyati; Arora, Anuja; Anand, Adarsh; Dwivedi, Yogesh
• Is Part Of: Kybernetes. Volume 50:Issue 6(2021)
• Page Start: 1811
• Page End: 1836
• Publication Date: 2020-08-03
• Subjects: Prediction -- YouTube -- Social networks -- Virality -- Information diffusion -- Popularity distribution -- Popularity dynamics -- Viewership; Cybernetics -- Periodicals; Systems engineering -- Periodicals; 003.505
• DOI: 10.1108/K-03-2020-0128
• Languages: English
• ISSNs: 0368-492X

Abstract

Abstract : Purpose: The purpose of this study/paper is to propose a mathematical model that is able to predict the future popularity based on the view count of a particular YouTube video. Since the emergence of video-sharing sites from early 2005, YouTube has been pioneering in its performance and holds the largest share of internet traffic. YouTube plays a significant role in popularizing information on social network. For all social media sites, viewership is an important and vital component to measure diffusion on a video-sharing site, which is defined in terms of the number of view counts. In the era of social media marketing, companies demand an efficient system that can predict the popularity of video in advance. Diffusion prediction of video can help marketing firms and brand companies to inflate traffic and help the firms in generating revenue. Design/methodology/approach: In the present work, viewership is studied as an important diffusion-affecting parameter pertaining to YouTube videos. Primarily, a mathematical diffusion model is proposed to predict YouTube video diffusion based on the varying situations of viewership. The proposal segregates the total number of viewers into two classes – neoterics viewers, i.e. viewers those viewing a video on a direct basis, and followers, i.e. viewers those watching under the influence. Findings: The approach is supplemented with numerical illustration done on the real YouTube data set. Results prove that the proposed approach contributes significantly to predict viewership of video. The proposed model brings predicted viewership and its classification highly close to the true value. Originality/value: Thereby, a behavioral rationale for the modeling and quantification is offered in terms of the two varied and yet connected classes of viewers – "neoterics" and "followers."