A Model Based Approach to Spotify Data Analysis: A Beta GLMM

18 Pages Posted: 24 Mar 2020

Date Written: March 19, 2020

Abstract

Digital music distribution is increasingly powered by automated mechanisms that continuously capture, sort and analyze large amounts of Web-based data. This paper deals with the management of songs audio features from a statistical point of view. In particular, it explores the data catching mechanisms enabled by Spotify Web API and suggests statistical tools for the analysis of these data. Special attention is devoted to songs popularity and a Beta model including random effects is proposed in order to give the first answer to questions like which are the determinants of popularity? The identification of a model able to describe this relationship, the determination within the set of characteristics of those considered most important in making a song popular is a very interesting topic for those who aim to predict the success of new products.

Keywords: Spotify Web API, audio features, Popularity Index, Beta GLMM

Suggested Citation

Sciandra, Mariangela and Spera, Irene Carola, A Model Based Approach to Spotify Data Analysis: A Beta GLMM (March 19, 2020). d/SEAS Working Paper Forthcoming. Available at SSRN: https://ssrn.com/abstract=3557124 or http://dx.doi.org/10.2139/ssrn.3557124

Mariangela Sciandra (Contact Author)

University of Palermo - d/SEAS

Viale delle Scienze, edificio 13
Palermo, 90124
Italy

Irene Carola Spera

Independent ( email )

No Address Available
United States

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