"All of Me": Mining Users' Attributes from their Public Spotify Playlists
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915673432653824 |
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| author | Tricomi, Pier Paolo Pajola, Luca Pasa, Luca Conti, Mauro |
| author_facet | Tricomi, Pier Paolo Pajola, Luca Pasa, Luca Conti, Mauro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14296 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | "All of Me": Mining Users' Attributes from their Public Spotify Playlists Tricomi, Pier Paolo Pajola, Luca Pasa, Luca Conti, Mauro Cryptography and Security Machine Learning Social and Information Networks 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. |
| title | "All of Me": Mining Users' Attributes from their Public Spotify Playlists |
| topic | Cryptography and Security Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2401.14296 |