A Model for Predicting Music Popularity on Streaming Platforms

The global music market moves billions of dollars every year, most of which comes from streamingplatforms. 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%

Examining bilateral music flows in the digital age: the role of distance in international music demand

This study explores the relationship between international music demand and consumer preferences in the music streaming era. An updated distance measure between countries to account for their music flow is proposed, considering the impact of digitization. The analysis shows that while traditional distance measures such as geographical proximity and shared language remain essential, cultural, and social distance are also significant. A composite measure is suggested to be more appropriate for accounting for consumer preferences in music streaming. The study provides insights into how the rise of digital music streaming has changed the pattern of international music demand