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

Mariangela Sciandra1, Irene Carola Spera2

  • 1Department of Economics, Business and Statistics, University of Palermo, Palermo, Italy.

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|June 16, 2022
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Summary
This summary is machine-generated.

This study uses statistical methods to analyze song popularity data from Spotify. It proposes a Beta model to identify key factors driving song success in digital music distribution.

Keywords:
6262H62PBeta GLMMSpotify web APIaudio featurespopularity index

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Area of Science:

  • Music Information Retrieval
  • Statistical Analysis
  • Data Science

Background:

  • Digital music distribution relies on automated systems for data analysis.
  • Understanding song popularity is crucial for predicting music success.

Purpose of the Study:

  • To statistically manage and analyze song audio features from web data.
  • To explore Spotify API data capturing mechanisms.
  • To identify determinants of song popularity using a statistical model.

Main Methods:

  • Utilized Spotify Web API for data acquisition.
  • Applied statistical tools for data analysis.
  • Developed a Beta model with random effects to study song popularity.

Main Results:

  • The study explores data capturing mechanisms of the Spotify Web API.
  • A Beta model was proposed to analyze song popularity determinants.
  • Identified key characteristics influencing a song's success.

Conclusions:

  • Statistical analysis of audio features can reveal popularity drivers.
  • The proposed Beta model offers insights into music success prediction.
  • This research is valuable for predicting the success of new music products.