"All of Me": Mining Users' Attributes from their Public Spotify
  Playlists
Pier Paolo Tricomi Luca Pajola Luca Pasa Mauro Conti
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. These
publicly accessible playlists transcend the boundaries of mere musical
preferences: they serve as sources of rich insights into users' attributes and
identities. For example, the musical preferences of elderly individuals may
lean more towards Frank Sinatra, while Billie Eilish remains a favored choice
among teenagers. These playlists thus become windows into the diverse and
evolving facets of one's musical identity.
  In this work, we investigate the relationship between Spotify users'
attributes and their public playlists. In particular, we focus on identifying
recurring musical characteristics associated with users' individual attributes,
such as demographics, habits, or personality traits. To this end, we conducted
an online survey involving 739 Spotify users, yielding a dataset of 10,286
publicly shared playlists encompassing over 200,000 unique songs and 55,000
artists. Through extensive statistical analyses, we first assess a deep
connection between a user's Spotify playlists and their real-life attributes.
For instance, we found individuals high in openness often create playlists
featuring a diverse array of artists, while female users prefer Pop and K-pop
music genres. Building upon these observed associations, we create accurate
predictive models for users' attributes, presenting a novel DeepSet application
that outperforms baselines in most of these users' attributes.