  Musical preferences have been considered a mirror of the self. In this age of
Big Data, online music streaming services allow us to capture ecologically
valid music listening behavior and provide a rich source of information to
identify several user-specific aspects. Studies have shown musical engagement
to be an indirect representation of internal states including internalized
symptomatology and depression. The current study aims at unearthing patterns
and trends in the individuals at risk for depression as it manifests in
naturally occurring music listening behavior. Mental well-being scores, musical
engagement measures, and listening histories of Last.fm users (N=541) were
acquired. Social tags associated with each listener's most popular tracks were
analyzed to unearth the mood/emotions and genres associated with the users.
Results revealed that social tags prevalent in the users at risk for depression
were predominantly related to emotions depicting Sadness associated with genre
tags representing neo-psychedelic-, avant garde-, dream-pop. This study will
open up avenues for an MIR-based approach to characterizing and predicting risk
for depression which can be helpful in early detection and additionally provide
bases for designing music recommendations accordingly.
