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. 2020 Aug 10;49(1):214-229.
doi: 10.1080/02664763.2020.1803810. eCollection 2022.

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

Mariangela Sciandra  1 , Irene Carola Spera  2
Affiliations

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

Mariangela Sciandra et al. J Appl Stat. .

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: 62; 62H; 62P; Beta GLMM; Spotify web API; audio features; popularity index.

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Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

Figure 1.
Figure 1.
Average values of Spotify features over time.
Figure 2.
Figure 2.
Distribution of songs according to the Popularity Index, conditioning to the album (13 November 2019).
Figure 3.
Figure 3.
Scatterplot of popularity vs. acousticness and danceability.
Figure 4.
Figure 4.
Scatterplot popularity vs. liveness and speechness.
Figure 5.
Figure 5.
Scatterplot popularity vs. time and duration.
Figure 6.
Figure 6.
Scatterplot popularity vs. valence and loudness.
Figure 7.
Figure 7.
Scatterplot popularity vs. instrumentalness and energy.
Figure 8.
Figure 8.
Random intercepts with 95 % confidence intervals.
See this image and copyright information in PMC

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