“All of Me”

Mining Users’ Attributes from their Public Spotify Playlists

Conference paper (2024)

Authors

Pier Paolo Tricomi Spritz Matter Srl, Università degli Studi di Padova

Luca Pajola Spritz Matter Srl, Università degli Studi di Padova

Luca Pasa Università degli Studi di Padova

M. Conti Università degli Studi di Padova

Affiliation

External organisation

Machine Learning Privacy Music Spotify User Profiling

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Published Date

2024

Language

English

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Affiliation

External organisation

Abstract

In the age of digital music streaming, playlists on platforms like Spotify have become an integral part of individuals’ musical experiences. People create and publicly share their own playlists to express their musical tastes, promote the discovery of their favorite artists, and foster social connections. In this work, we aim to address the question: can we infer users’ private attributes from their public Spotify playlists? To this end, we conducted an online survey involving 739 Spotify users, resulting in a dataset of 10,286 publicly shared playlists comprising over 200,000 unique songs and 55,000 artists. Then, we utilize statistical analyses and machine learning algorithms to build accurate predictive models for users’ attributes.