Sensitivity Analysis using Regression Models: To Determine the Impact of Meta-level Features on the Youtube Views

Vaishnavi Borwankar, Catherine Chris, Hitesh Kumar, Sophia Rahaman

2022

Abstract

The popularity of social media has led to a shift in paradigm with YouTube emerging as a ubiquitous platform for networking and content sharing. YouTube, with over a million content creators, has become the most preferred destination for watching videos online. The meta-level features like the title, tags, number of views, likes, dislikes, etc. are significant to determine the sensitivity of the videos. This study aims to determine how these meta-level features can better be utilized to increase the popularity of the videos. The study specifically analyzes how the number of likes, dislikes and comment count have an impact on the number of views. The number of likes, dislikes and the comment count are the independent variables, while the view count is a dependent variable. The dataset used for this research is the daily Trending YouTube Video Statistics for the years 2017-2019 from Kaggle, that spans across the US region with over forty-thousand videos from sixty plus channels released by YouTube for public use. In this paper, we use the Ordinary Least Square Regression Algorithm and Stochastic Gradient Descent Algorithm to perform Sensitivity Analysis. The analysis is performed on two categories: Media and Sports. The accuracy of both the models are compared by evaluating the mean absolute error (MAE) and the relative absolute error (RAE) taken from the results of the experiment. The results showed a significant impact of meta-level features on the popularity of the videos along with their percentage dependency.

Paper Citation

in Harvard Style

Borwankar V., Chris C., Kumar H. and Rahaman S. (2022). Sensitivity Analysis using Regression Models: To Determine the Impact of Meta-level Features on the Youtube Views. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-583-8, pages 270-275. DOI: 10.5220/0011207300003269

in Bibtex Style

@conference{data22,
author={Vaishnavi Borwankar and Catherine Chris and Hitesh Kumar and Sophia Rahaman},
title={Sensitivity Analysis using Regression Models: To Determine the Impact of Meta-level Features on the Youtube Views},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2022},
pages={270-275},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011207300003269},
isbn={978-989-758-583-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Sensitivity Analysis using Regression Models: To Determine the Impact of Meta-level Features on the Youtube Views
SN - 978-989-758-583-8
AU - Borwankar V.
AU - Chris C.
AU - Kumar H.
AU - Rahaman S.
PY - 2022
SP - 270
EP - 275
DO - 10.5220/0011207300003269