Static and Dynamic Measures of Active Music Listening as Indicators of Depression Risk

TL;DR

This study investigates how static and dynamic patterns in active music listening behavior, including acoustic content and emotional features, relate to depression risk, revealing that higher depression correlates with more repetitive and sad music preferences.

Contribution

It introduces a novel analysis of time-varying music consumption patterns and their association with mental well-being, using digital footprints from online music streaming data.

Findings

Depressed individuals show higher dependency on repetitive music.

Sad music preference remains stable over time in depressed users.

Repetitiveness in listening activity correlates with depression risk.

Abstract

Music, an integral part of our lives, which is not only a source of entertainment but plays an important role in mental well-being by impacting moods, emotions and other affective states. Music preferences and listening strategies have been shown to be associated with the psychological well-being of listeners including internalized symptomatology and depression. However, till date no studies exist that examine time-varying music consumption, in terms of acoustic content, and its association with users' well-being. In the current study, we aim at unearthing static and dynamic patterns prevalent in active listening behavior of individuals which may be used as indicators of risk for depression. Mental well-being scores and listening histories of 541 Last.fm users were examined. Static and dynamic acoustic and emotion-related features were extracted from each user's listening history and correlated with their mental well-being scores. Results revealed that individuals with greater depression risk resort to higher dependency on music with greater repetitiveness in their listening activity. Furthermore, the affinity of depressed individuals towards music that can be perceived as sad was found to be resistant to change over time. This study has large implications for future work in the area of assessing mental illness risk by exploiting digital footprints of users via online music streaming platforms.