6533b820fe1ef96bd12799e7
RESEARCH PRODUCT
A model-based approach to Spotify data analysis: a Beta GLMM
Irene Carola SperaMariangela Sciandrasubject
Statistics and ProbabilityBeta GLMMDistribution (number theory)Computer scienceApplication Notes0211 other engineering and technologies02 engineering and technologycomputer.software_genreWeb API01 natural sciencesSet (abstract data type)010104 statistics & probabilitySpotify Web API audio features Popularity Index Beta GLMMsortSpotify Web API0101 mathematicsDigital audio021103 operations researchPoint (typography)Random effects modelData sciencePopularityIdentification (information)Popularity IndexData miningStatistics Probability and Uncertaintycomputeraudio featuredescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-08-10 |