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

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://www.youtube.com/watch?v=-6ooQbTtOqc. The DS-II data that support the findings of this study are openly available at https://www.youtube.com/watch?v=dgyPZv8LdS8. 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.