A Model for Predicting Music Popularity on Streaming Platforms

Carlos Vicente Soares Araujo, Marco Antônio Pinheiro de Cristo, Rafael Giusti

Abstract


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%.

Keywords


Music; Hit Song Science; Machine Learning; Spotify

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DOI: https://doi.org/10.22456/2175-2745.107021

Copyright (c) 2020 Carlos Vicente Soares Araujo, Marco Antônio Pinheiro de Cristo, Rafael Giusti

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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