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

Mariangela Sciandra, Irene Carola Spera

Research output: Contribution to journalArticlepeer-review


Digital music distribution is increasingly powered by automated mechanisms that continuouslycapture, sort and analyze large amounts of Web-based data. This paper deals with the managementof songs audio features from a statistical point of view. In particular, it explores the datacatching mechanisms enabled by Spotify Web API, and suggests statistical tools for the analysis ofthese data. Special attention is devoted to songs popularity and a Beta model including random effectsis 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 setof characteristics of those considered most important in making a song popular is a very interestingtopic for those who aim to predict the success of new products.
Original languageEnglish
Number of pages18
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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