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

Mariangela Sciandra, Irene Carola Spera

Research output: Contribution to journalArticle

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 a 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.
Original languageEnglish
Number of pages17
JournalJournal of Applied Statistics
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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