Exploring Musical, Lyrical, and Network Dimensions of Music Sharing
Among Depression Individuals
- URL: http://arxiv.org/abs/2310.11557v1
- Date: Tue, 17 Oct 2023 20:08:43 GMT
- Title: Exploring Musical, Lyrical, and Network Dimensions of Music Sharing
Among Depression Individuals
- Authors: Qihan Wang, Anique Tahir, Zeyad Alghamdi, Huan Liu
- Abstract summary: Social media has become an important platform for individuals navigating through depression.
This study explores the differences in music preferences between individuals diagnosed with depression and non-diagnosed individuals.
- Score: 14.293723126727485
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Depression has emerged as a significant mental health concern due to a
variety of factors, reflecting broader societal and individual challenges.
Within the digital era, social media has become an important platform for
individuals navigating through depression, enabling them to express their
emotional and mental states through various mediums, notably music.
Specifically, their music preferences, manifested through sharing practices,
inadvertently offer a glimpse into their psychological and emotional
landscapes. This work seeks to study the differences in music preferences
between individuals diagnosed with depression and non-diagnosed individuals,
exploring numerous facets of music, including musical features, lyrics, and
musical networks. The music preferences of individuals with depression through
music sharing on social media, reveal notable differences in musical features
and topics and language use of lyrics compared to non-depressed individuals. We
find the network information enhances understanding of the link between music
listening patterns. The result highlights a potential echo-chamber effect,
where depression individual's musical choices may inadvertently perpetuate
depressive moods and emotions. In sum, this study underscores the significance
of examining music's various aspects to grasp its relationship with mental
health, offering insights for personalized music interventions and
recommendation algorithms that could benefit individuals with depression.
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