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A model-based approach to Spotify data analysis: a Beta GLMM

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  • Mariangela Sciandra
  • Irene Carola Spera

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.

Suggested Citation

  • Mariangela Sciandra & Irene Carola Spera, 2022. "A model-based approach to Spotify data analysis: a Beta GLMM," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(1), pages 214-229, January.
    • Handle: RePEc:taf:japsta:v:49:y:2022:i:1:p:214-229
    DOI: 10.1080/02664763.2020.1803810
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    Cited by:

    1. Graziella Bonanno & Filippo Domma & Lucia Errico, 2022. "Income Inequality And Inner Areas. A Study On The Italian Case," Working Papers 202203, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.

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