A Model for Predicting Music Popularity on Streaming Platforms


  • Carlos Vicente Soares Araujo Federal University of Amazonas
  • Marco Antônio Pinheiro de Cristo Federal Univeristy of Amazonas
  • Rafael Giusti Federal University of Amazonas




Music, Hit Song Science, Machine Learning, Spotify


The global music market moves billions of dollars every year, most of which comes from streaming
platforms. In this paper, we present a model for predicting whether or not a song will appear in Spotify’s Top 50, a ranking of the 50 most popular songs in Spotify, which is one of today’s biggest streaming services. To make this prediction, we trained different classifiers with information from audio features from songs that appeared in this ranking between November 2018 and January 2019. When tested with data from June and July 2019, an SVM classifier with RBF kernel obtained accuracy, precision, and AUC above 80%.


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Author Biographies

Carlos Vicente Soares Araujo, Federal University of Amazonas

Institute of Computing

Marco Antônio Pinheiro de Cristo, Federal Univeristy of Amazonas

Institute of Computing

Rafael Giusti, Federal University of Amazonas

Institute of Computing


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How to Cite

Soares Araujo, C. V., Pinheiro de Cristo, M. A., & Giusti, R. (2020). A Model for Predicting Music Popularity on Streaming Platforms. Revista De Informática Teórica E Aplicada, 27(4), 108–117. https://doi.org/10.22456/2175-2745.107021



Selected Papers - SBCM 2019