2024/01/25 by Pier Paolo Tricomi, Luca Pajola, Tricomi, Pier Paolo +5 · 1 citation
Arts and Humanities · Social Sciences · #Asian Culture and Media Studies #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Impact of Technology on Adolescents #Machine Learning (cs.LG) #Music History and Culture #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2401.14296
openalex publication_date 2024/01/25 · openalex created_date 2024/01/27 · openalex updated_date 2026/07/28
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.