Selection of Optimal YouTube View Count Prediction Model Using Data Envelopment Analysis Approach

Garima Babbar; Adarsh Anand; Mohini Agarwal

doi:10.47852/bonviewJCCE42023120

Authors

Garima Babbar Department of Operational Research, University of Delhi, India https://orcid.org/0000-0002-4083-4777
Adarsh Anand Department of Operational Research, University of Delhi, India https://orcid.org/0000-0003-2733-3967
Mohini Agarwal College of Humanities and Social Sciences, Grand Canyon University, United States https://orcid.org/0000-0001-5669-8936

DOI:

https://doi.org/10.47852/bonviewJCCE42023120

Keywords:

Data Envelopment Analysis (DEA), Multiple Criteria Decision Making (MCDM), social media, view count, YouTube

Abstract

This study investigates the dynamics of video view counts on YouTube to gain a comprehensive understanding of its influence on digital media engagement. By analyzing the impact of increasing parameters in view count models and employing various distribution functions for the viewing rate, the research employs a unified approach to assess model responses to a broader set of parameters. This methodology integrates different distribution functions, resulting in a range of models that capture diverse viewing behavior patterns. The effectiveness of these models is evaluated using Data Envelopment Analysis (DEA), a robust analytical tool within the framework of Multiple Criteria Decision Making (MCDM) techniques. The results highlight that models with two parameters exhibit superior efficiency across multiple datasets, effectively representing the complex dynamics of YouTube’s view count patterns. This study advances the academic discourse on digital platforms by offering a detailed analysis of YouTube’s view count dynamics. The results provide actionable insights for customizing content and engagement strategies to match observed viewing behavior patterns, thereby improving content dissemination and audience interaction on the platform.

Received: 15 April 2024 | Revised: 12 June 2024 | Accepted: 25 June 2024

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The DS-I data that support the findings of this study are openly available at https://youtu.be/24-YonhNS0Y. The DS-II data that support the findings of this study are openly available at https://youtu.be/S7eJes8AirA/hetrec-2011/. The DS-III data that support the findings of this study are openly available at https:// youtu.be/MmlJb0Pi2-0. The DS-IV data that support the findings of this study are openly available at https://youtu.be/97AE_mAlhhc.

Author Contribution Statement

Garima Babbar: Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Funding acquisition. Adarsh Anand: Conceptualization, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Mohini Agarwal: Methodology, Software.