# Exploring Musical, Lyrical, and Network Dimensions of Music Sharing Among
Depression Individuals

Qihan Wang<EMAIL_ADDRESS>Aalto UniversityEspooFinland02150 ,  Anique
Tahir<EMAIL_ADDRESS>Arizona State UniversityTempeArizonaUSA85281 ,  Zeyad
Alghamdi<EMAIL_ADDRESS>Arizona State UniversityTempeArizonaUSA85281 and
Huan Liu<EMAIL_ADDRESS>Arizona State UniversityTempeArizonaUSA85281

(2018; 20 February 2007; 12 March 2009; 5 June 2009)

###### 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.

††copyright: acmcopyright††journalyear: 2018††doi:
XXXXXXX.XXXXXXX††conference: Make sure to enter the correct conference title
from your rights confirmation emai; June 03–05, 2018; Woodstock, NY††price:
15.00††isbn: 978-1-4503-XXXX-X/18/06

##  1\. Introduction

Depression is a prevalent mental disorder characterized by persistent feelings
of sadness, a diminished interest, or a lack of pleasure in daily activities
(Gotlib and Hammen, 2008). Depression impacts approximately 3.8% of the global
population across all ages, genders, and cultural groups (Leubner and
Hinterberger, 2017), posing a significant worldwide challenge (WHO, 2023).
Distinct from regular mood fluctuations, depression is characterized by its
prolonged duration, and may manifest in feelings of excessive guilt or low
self-worth, loss of interest or pleasure, hopelessness about the future,
disturbed sleep or appetite, and poor concentration (Woody et al., 2017;
Evans-Lacko et al., 2018). With the development of social media, individuals
experiencing depression tend to express their thoughts and share their
feelings with their peers and audiences online (Andalibi et al., 2017). The
shared content includes a variety of forms, such as text, photos, news and
music.

Music, intertwining with daily lives, enables individuals to regulate
emotions, maintain interpersonal relationships, and express their identities
(Lee et al., 2011; Johnson and Ranzini, 2018). As people connect their own
memories and thoughts with music (Krumhansl, 2002), music sharing can serve as
a mirror to reflect people’s moods and feelings towards life events (Robinson,
2005; Greasley and Lamont, 2006). Building on this, individuals with
depression may perceive music differently and exhibit distinct musical
preferences. Previous psychology research has shown that individuals with a
tendency towards depression demonstrated a preference for sad music (Garrido
and Schubert, 2015), and individuals who are diagnosed by depression perceive
music as conveying more negatively emotion (Eerola and Vuoskoski, 2011).
However, most of the previous research on music and depression focused on the
emotion of the songs, ignoring other musical features that can be quantified
or measured by rhythmic and structural characteristics, such as tempo, energy,
mode and acousticness. In addition, they often overlook elements beyond
musical features, such as lyrics and associated networks.

In sum, while prior investigations have primarily studied the emotional
aspects of songs, the broader spectrum of musical attributes, lyrics, and
associated networks largely remains unexplored. This significant gap presents
a chance to explore in more detail the differences of music preferences
between individuals with and without depression. Given this context, our study
takes a broad approach, carefully examining not just various musical features,
but also delving into lyrics and network characteristics among social media
users. Based on a publicly available dataset (Kag, [n. d.]) that encompasses
music sharing data from users who have self-expressed that they have been
diagnosed with depression, together with data from randomly-selected control
users (non-depression users), we studied 4 research questions:

RQ1: Do musical features of music sharing differ between depression and non-
depression individuals? By collecting musical features of each song from
Spotify API (Spo, 2023), we discern any notable variations between the
depression and non-depression cohorts. We found that songs shared by
depression are significant higher in energy, mode, acousticness,
instrumentalness, and duration, and lower in speechiness, time signature,
popularity and danceability.

RQ2: What are specific artists or genres that are commonly shared by
depression and non-depression individuals? We presented qualitative
visualizations for artists and genres shared by depression groups and control
groups, and leveraged TF-IDF to present artists and genres that are uniquely
shared by depression groups.

RQ3: Do topics and language use in lyrics of music sharing differ between
depression and non-depression individuals? We conducted a multi-faceted lyric
level analysis. Our result showed that music shared by individuals with
depression have higher proportion in the topic of emotional reflection and
religious language, yet lower proportion in the topic of explicit (RAP)
languages and personal relationships. Furthermore, individuals with depression
prefer lyrics with shorter sentences.

RQ4: What are the network relationships regarding music shared by depression
and non-depression individuals? Based on Spotify Playlist Dataset (Spo, [n.
d.]), we categorized playlists as depression/control playlists by the amount
of songs that shared by depression users and conducted community detection for
all playlists. Our result indicated that the depression playlists cluster
together, suggesting a network connection between depression songs. Combined
and coherent with the previous lyric analysis, we found that emotional
reflection and religious or biblical language are dominant topics in
depression playlist communities.

In synthesizing our findings, we discern crucial implications across two
pivotal dimensions. Firstly, our results demonstrated social and cognitive
characteristic features of individuals with depression in relation to their
music preferences. Secondly, we delved into the consequences engendered by
these specific musical preferences. We found that the music preference of
individuals with depression potentially encapsulates users within a depression
Echo-Chamber Effect, where the music exacerbates prevailing depressive moods
and emotions. This explorative analysis not only heightened our understanding
of the nuanced relationships between music preferences and mental health but
also illuminated pathways for the development of music recommendation
algorithms that were attuned to the needs of individuals navigating mental
health disorders.

##  2\. Related Work

Music, an integral component of human interaction, has seen its influence
magnified due to advances in digital technology, especially on platforms like
social media (Kassabian, 2013; Jones, 2000). These platforms have become
central stages where music is shared, offering insights into individuals’
identity and self-presentation (Johnson and Ranzini, 2018; Valverde, 2022). In
this work, we focus on its relationship with mental states, particularly
depression. Specifically, we explore the multifaceted connections between
music sharing and depression, including musical features, lyrics, and social
network relationships. This section aims to provide an overview, drawing from
existing research to shape our discourse on these interconnected themes.

###  2.1. Music Features and Its Multifaceted Connection to Individuals

Our study aims to incorporate a diverse array of musical features, providing a
more comprehensive understanding of the music preferences of individuals with
depression. Spanning from rhythmic and structural to timbral characteristics,
these features serve as quantitative representations of various dimensions of
music, enabling a wide range of applications such as music recommendation,
playlist generation, and exploratory musicological research (Spo, 2023). These
features encompass various attributes such as danceability, duration, energy,
instrumentalness, liveness, loudness, speechiness, tempo, and valence (Barone
et al., 2017; Greenberg et al., 2016). Each feature encapsulates a distinct
aspect of a song, elucidating its various musical and acoustical properties;
for instance, danceability refers to how suitable a track is for dancing,
often through examining elements like beat regularity and strength, and
valence serves to describe the musical positiveness conveyed by a track,
distinguishing between sonically positive (e.g., happy, cheerful) and negative
(e.g., sad, depressive) emotional expressions. Moreover, research have found
that there’s a notable correlation between these musical features and
individuals’ traits: individual’s personality aligns with their affinity or
preference for specific musical features, including mode, tempo, and dynamics
(Flannery and Woolhouse, 2021; Ferwerda et al., 2015).

###  2.2. Overview of Depression and Music

Music preference is intricately linked with an individual’s conditions and
traits, including mental health conditions (Lester and Whipple, 1996),
personality (Delsing et al., 2008), and intelligence (Bonetti and Costa,
2016). Specifically, when examining the connection with depression, past
research has predominantly centered on musical genres and sentiments. In the
context of genres, prior studies suggest that listening to soul music (hip hop
and R&B) is a predictor of lower depression levels (Miranda and Claes, 2008).
As for sentiment, previous research has demonstrated that individuals
diagnosed with depression perceive music as more negatively valence (Eerola
and Vuoskoski, 2011). Such individuals exhibit a tendency to gravitate towards
negatively valenced music, mirroring their chronic mood state (Wilhelm et al.,
2013). Moreover, listening to sad music can significantly increase the
depressed feelings of individuals with depression (Garrido and Schubert,
2015).

Nevertheless, music has also been identified as a therapeutic tool for
individuals with depression. Studies indicate that music listening can lead to
a notable reduction in depression levels (Maratos et al., 2008). This
therapeutic approach, termed music therapy, has been corroborated as an
efficacious treatment for depression (Erkkilä et al., 2011).

###  2.3. Overview of Depression and Social Media

Recent research increasingly harnesses user-generated content on social media
to explore mental health-related issues, generally employing two primary
approaches: (1) analyzing the behavior patterns, cognition, and emotional
aspects of mental health disorders (Xu and Zhang, 2016), and (2) developing
automatic detection systems for mental health problems or anomalies (De
Choudhury et al., 2013b; Guntuku et al., 2017). These studies broadly utilizes
existing tools and methods from data mining, natural language processing,
social network analysis, and machine learning. Within this framework, a
spectrum of mental health disorders, including major depressive disorders (De
Choudhury et al., 2013b), eating disorders (Frieiro Padin et al., 2021), and
ADHD (Guntuku et al., 2019), have been explored. Our work is anchored in the
first approach, and it aims to discern the variance in music-sharing behaviors
between individuals diagnosed with depression and those who aren’t.

Building upon the foundation of the first approach, earlier research focusing
on depression and its manifestation on social media has delved into aspects
like social interaction, linguistic expression, and emotional displays of
affected users. From a sociological standpoint, it’s been observed that
individuals with depression tend to manifest a decrease in social activity and
form highly clustered ego-networks (De Choudhury et al., 2013a). From
linguistical perspectives, it has been shown that individuals with depression
express more distorted thinking, and their online language is generally deemed
depressogenic (Bathina et al., 2021). Notably, the linguistic patterns and
topic-centric vocabulary of depressed individuals significantly deviate from
their non-depressed counterparts (Ríssola et al., 2022). On the emotional
front, it’s been found that self-reported depression is related to the usage
of personal and negative pronoun language categories (Liu et al., 2022b).
Furthermore, individuals with depression form more negative sentences from
scrambled words. In other words, they tend to form more pessimistic sentences
than non-depressed participants, even when they are not in a negative mood
(Wenzlaff and Bates, 1998). Moreover, from topic perspectives, people with
depression have amplified relational and medicinal concerns and express
greater religious involvement (De Choudhury et al., 2013a). While previous
research has established a discrepancy in the behavior and expression between
individuals with and without depression on social media, spanning sociality,
language expression, and emotion, a gap persists in understanding the domain
of music preferences. Our study aspires to bridge this gap, aiming to discern
how music sharing practices diverge between these two groups, consequently
presenting a fresh lens to the existing discourse.

##  3\. Evaluation & Results

In this section, we present the findings obtained from our analysis focusing
on three primary domains: musical variables, lyrics and network. Our
objectives were to: (1) identify significant differences in musical variables,
artists and genres between depression and non-depression groups by utilizing
statistical analysis (2) present artist and genres differences qualitatively
by word cloud (3) understand the difference of topics and language use of the
lyrics between depression and non-depression groups by LDA and LIWC (4)
explore the connection of depression playlists by leveraging community
detection.

###  3.1. Data Collection

To achieve our research objectives, we carefully selected a dataset that
aligns with the intersection of mental health and musical engagement, which is
a crucial part of our investigation. But also aiding us with a notable
advantage, that is, the inclusion of a depression and a control group, which
provides a foundational comparison and paves the way for a deeper and more
comprehensive analysis of the interplay between music-sharing behaviors and
mental states in a digital context. Therefore, we sourced an anonymized
publicly available dataset that encompasses social media information (Kag, [n.
d.]). In our selected dataset, the ’Depression Group’ is characterized by
users who have self-identified with a depression diagnosis on Twitter, while
the ’Control Group’, also known as ’Non-Depression Group’, includes those who
never tweeted depression or any other mental disorders. Every entry in this
dataset provides specifics like the song’s title, artist, and cleaned-up
lyrics. There’s also a marker to identify whether the song-sharer publicly
disclosed a depression diagnosis on Twitter. We further ensured a balanced
representation of music-sharing posts between both groups. For a richer
analysis of the link between music-sharing and depression, we tapped into the
Spotify API (Spo, 2023; Panda et al., 2021) to extract musical attributes for
each shared piece. This extraction process yielded 14 numerical variables,
which are described in table 1. Each variable reveals a unique facet of the
musical piece. Given the wide spectrum of genres a song can belong to, the
collective attributes present a panoramic perspective on musical styles and
their potential implications for understanding an individual’s psychological
state.

Table 1. Description of Numerical Variables from Spotify API Variable |  Description
---|---
Danceability |  Danceability is a measure from 0.0 to 1.0 which describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity.
Energy |  Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy.
Key |  The key the track is in, using standard Pitch Class notation.
Loudness |  Overall loudness of a song in decibels (dB).
Mode |  Modality of a track; 1 represents major and 0 represents minor.
Speechiness |  Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value.
Acousticness |  A confidence measure from 0.0 to 1.0 of whether the track is acoustic.
Instrumentalness |  Predicts whether a track contains no vocals.
Liveness |  Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live.
Valence |  A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track.
Tempo |  The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
Duration_ms |  Duration of the song in milliseconds.
Time_Signature |  Estimated overall time signature of a track, which specify how many beats are in each bar
Popularity |  Measure of the track’s popularity based on number of plays.

For network analysis, we leverage the Spotify Million Playlist Dataset (Spo,
[n. d.]), which contains titles for songs and playlists created by users on
the Spotify platform between January 2010 and October 2017. We aimed to
examine if playlists with songs linked to depression cluster distinctively
compared to those without such songs, aiming to shed light on the intricate
relationship between music preferences and mental health.

###  3.2. Musical Features

In addressing RQ1 through our exploration of musical features, we aimed to
identify any significant differences in musical variables between depression
and non-depression groups. Our null hypothesis, denoted as $H_{0}$, posits no
significant difference in the evaluated musical attributes between the two
cohorts. We subjected this hypothesis to empirical verification employing a
suite of statistical tests. We found that there are significant differences
between depression and control group in danceability ($MD=-0.041$, $p<0.001$),
energy ($MD=-0.012$, $p<0.001$), speechiness ($MD=-0.030$, $p<0.001$),
acousticness ($MD=0.009$, $p<0.05$), instrumentalness ($MD=0.022$, $p<0.001$),
Time Signature ($MD=-0.026$, $p<0.001$), popularity ($MD=-1.000$, $p<0.001$).
Moreover, the duration of the song is significantly longer in depression group
($MD=8875.318$, $p<0.001$). On the other hand, there are significantly more
major mode and less minor mode in depression group ($MD=0.044$,
$p<0.001$).Additionally, there is no difference in key, loudness, liveness,
valence, and tempo.

Table 2. Table 2: Statistical Analysis of Musical Difference

Variable | T-value | df | MD | p-value
---|---|---|---|---
danceability | -17.070585 | 18262 | -0.041 | 0.000∗∗∗
energy | 4.03491 | 18262 | 0.012 | 0.000∗∗∗
key | -0.127663 | 18262 | -0.007 | 0.898
loudness | -2.202757 | 18262 | -0.102 | 0.028∗
mode | 6.087954 | 18262 | 0.044 | 0.000∗∗∗
speechiness | -18.630115 | 18262 | -0.030 | 0.000∗∗∗
acousticness | 2.229929 | 18262 | 0.009 | 0.026∗
instrumentalness | 8.837124 | 18262 | 0.022 | 0.000∗∗∗
liveness | 0.815591 | 18262 | 0.002 | 0.415
valence | -0.700019 | 18262 | -0.002 | 0.484
tempo | 1.768637 | 18262 | 0.778 | 0.077
duration_ms | 7.613851 | 18262 | 8875.318 | 0.000∗∗∗
time_signature | -4.905537 | 18262 | -0.026 | 0.000∗∗∗
popularity | -3.622495 | 18262 | -1.000 | 0.000∗∗∗

∗ refers to the significance(∗ $p<0.05$ , ∗∗ $p<0.01$ , ∗∗∗ $p<0.001$)

In addition to the quantitative analysis for artists and genres for answering
RQ2, we also employ qualitative visualizations to intuitively represent the
prevalent elements in our dataset.

Specifically, we construct word clouds of depression and control group for
artists (figure 1(a) and 1(b)) and genres (figure 1(d) and 1(e)). By
leveraging TF-IDF, we present the unique artist 1(c) and genres 1(f) in
depression group.

(a) Artist of Depression Group

(b) Artist of Control Group

(c) Unique Artist Items in Depression

(d) Genre of Depression Group

(e) Genre of Control Group

(f) Unique Genres Items in Depression

Figure 1. Qualitative Visualization of Artist and Genres

###  3.3. Lyrics Analysis

Although the analysis of musical features have provided invaluable insights,
there remains an intricate layer of understanding that can only be unraveled
through an examination of lyrics. Lyrics, imbued with semantic and emotional
content, serve as a conduit for artists to express intricate sentiments, and
for listeners to resonate with these expressions. The lyrical content can
amplify, complement, or even contrast the emotional tenor conveyed by musical
elements. Given that individuals with depression may be particularly attuned
to the emotional and thematic content of lyrics(add citation), a granular
analysis at the lyric level is paramount. It facilitates a more comprehensive
understanding of the specific themes, narratives, and emotional expressions
that resonate with this demographic. This, in turn, could unveil nuanced
patterns and correlations, offering a richer, multidimensional perspective on
the intricate interplay between music, lyrics, and mental health, and
potentially informing targeted, lyrics-based interventions for individuals
with depression.

In pursuit of RQ3 that focusing on the lyrics differences, we conducted multi-
faceted lyric level analysis. We use traditional topic modelling to
distinguish clusters at a linguistic level. In addition, we use LIWC based
approach to analyze the lyrics at a more emotional, cognitive, and structural
level.

####  3.3.1. Topic Modeling

In our lyrics analysis, we employ Latent Dirichlet Allocation (LDA) to
identify latent topics from the corpus of song lyrics, aiming to unveil
thematic distinctions between the depression and control groups. LDA is a
generative probabilistic model in which each document (in this case, a set of
lyrics) is represented as a random mixture of latent topics (Blei et al.,
2003). Each topic is characterized by a distribution over words in the
vocabulary.

Given a corpus of lyrics $D$, each song’s lyrics $d\in D$ is modeled as a
finite mixture over an underlying set of topics. Each topic $k$, represented
as $\beta_{k}$, is a distribution over the vocabulary of words. The topics are
assumed to have been drawn from a Dirichlet distribution with parameter
$\eta$:

$\beta_{k}\sim\text{Dir}(\eta),\quad k=1,\ldots,K.$

Each document (or set of lyrics) $d$ is associated with a vector of topic
proportions $\theta_{d}$, which is drawn from a Dirichlet distribution with
parameter $\alpha$:

$\theta_{d}\sim\text{Dir}(\alpha),\quad d=1,\ldots,D.$

Each word $w_{di}$ in document $d$ is generated by first sampling a topic
$z_{di}$ from the multinomial distribution parameterized by $\theta_{d}$, and
then sampling the word from the multinomial distribution parameterized by
$\beta_{z_{di}}$:

$z_{di}\sim\text{Multinomial}(\theta_{d}),\quad
w_{di}\sim\text{Multinomial}(\beta_{z_{di}}).$

We use the estimated topic-word distributions and document-topic distributions
to analyze the thematic content of lyrics in both the depression and control
groups. Statistical tests, such as the Proportion Z-Test, are then applied to
evaluate the significance of the observed differences in topic proportions
between the two groups. The most common 15 topics in the lyrics are shown in
table 3

The Proportion Z-Test is employed to assess whether the proportions of the
topic differ significantly between depression and control group. Compared to
the control group, there is a statistically significant higher proportion of
Music Recording($p<0.05$, $MD=0.002$), Religious or Biblical Language
($p<0.01$, $MD=0.005$), Everyday Life ($p<0.01$, $MD=0.0039$), Entertainment
Industry($p<0.01$, $MD=0.002$), Recollection and Creativity($p<0.05$,
$MD=0.003$), Everyday Experiences ($p<0.001$, $MD=0.036$), Emotional
Reflection ($p<0.001$, $MD=0.093$), Everyday Conversations($p<0.01$,
$MD=0.004$) in the depression group, and there is a statistically significant
lower proportion of Explicit or RAP Language ($p<0.001$, $MD=-0.084$),
Expressive Language($p<0.001$, $MD=-0.0403$), Musical Artists and
Collaborations($p<0.001$, $MD=-0.007$), and Personal Relationships ($p=0.01$,
$MD=-0.010$) in the depression group.

Table 3. Topic Modeling Result Topic | Interpretaion | Most representative topic
---|---|---
0 | Music Recording | studio, recorded, nyc, gun, machine
1 | Religious or Biblical Language | god, king, thou, said, man
2 | Everyday Life | people, going, year, like, school, work
3 | Entertainment Industry | taylor, award, music, batman, man
4 | Love and Relationships | baby, love, like, want, girl
5 | Political or Philosophical Discussion | great, man, people, power, state
6 | Conversational Language | say, tell, know, dont, like
7 | Explicit or RAP Language | shit, bitch, fuck, nigga, money
8 | Recollection and Creativity | remember, love, night, christmas, summer
9 | Expressive Language | dont, know, love, like, want
10 | Individual Names and Personal Interactions | stephen, zoe, man, old, say
11 | Time and Personal Relations | love, january, april, young, july
12 | Timeframes and Specific Events | january, march, april, february, june
13 | Legal and Constitutional Discourse | state, court, law, power, congress
14 | Everyday Experiences | like, come, life, home, hand
15 | Musical Artists and Collaborations | feat, lil, west, kanye, travis
16 | Emotional Reflection | time, away, heart, love, way
17 | Explicit or Medical Words | nigga, health, secretary, bitch, service
18 | Personal Relationships | love, like, young, boy, girl
19 | Everyday Conversations | like, said, time, day, know

####  3.3.2. LIWC Across Single-Occurrence and Repeated Songs

To explore the characteristics of songs, we employed the Linguistic Inquiry
and Word Count (LIWC) tool (Boyd et al., 2022). This tool provides features
that capture the text’s linguistic, emotional, cognitive, and structural
elements. We designed two subsets of the original dataset, one for each of the
depression and control groups, while eliminating overlapping songs to maintain
methodological rigor.

For a given lyric $L$, LIWC generates a feature vector as follows:

$F(L)=\begin{bmatrix}f_{\text{linguistic}}(L)\\\ f_{\text{emotional}}(L)\\\
f_{\text{cognitive}}(L)\\\ f_{\text{structural}}(L)\end{bmatrix}$

Where:

$\displaystyle f_{\text{linguistic}}$ $\displaystyle:\text{Linguistic
attributes of }L$ $\displaystyle f_{\text{emotional}}$
$\displaystyle:\text{Emotional tone and sentiment in }L$ $\displaystyle
f_{\text{cognitive}}$ $\displaystyle:\text{Cognitive complexity within }L$
$\displaystyle f_{\text{structural}}$ $\displaystyle:\text{Structural and
grammatical aspects of }L$

These feature vectors facilitate nuanced comparisons between the ’control’ and
’depression’ groups, enriching our understanding of their linguistic and
emotional preferences.

The mean of each LIWC feature for both classes was calculated, followed by the
computation of the Mean Difference (MD):

(1)
$\text{MD}=\frac{1}{m}\sum_{i=1}^{m}x_{i,\text{Control}}-\frac{1}{n}\sum_{j=1}^{n}x_{j,\text{Depression}}$

Here, $m$ and $n$ represent the sample sizes of the ”control” and ”depression”
classes, respectively. $x_{i,\text{Control}}$ and $x_{j,\text{Depression}}$
are the specific feature values in each class.

Finally, we identified the top 10 features with the most significant mean
differences, specifying the directionality to indicate whether the ”control”
or ”depression” group is favored.

  * •

Word Count (WC) (+360.38): More complex narratives in the depression group.

  * •

Words Per Sentence (WPS) (-14.73): Simpler sentences in the depression group.

  * •

Clout (+2.70): Greater social influence in lyrics favored by the depression
group.

  * •

Punctuation (AllPunc) (-1.46): Less formal writing styles in the depression
group.

  * •

Function Words (Function) (+1.25): More complex cognitive processing in the
depression group.

  * •

Linguistic Processes (Linguistic) (+1.05): Intricate language use in the
depression group.

  * •

Dictionary Words (Dic) (+1.00): Broader vocabulary in the depression group.

  * •

Comma Usage (Comma) (-0.84): Less intricate sentences in the depression group.

  * •

Big Words (BigWords) (+0.75): Preference for complex words in the depression
group.

  * •

Authenticity (Authentic) (+0.71): Slight preference for authentic lyrics in
the depression group.

###  3.4. Network Analysis

To answer RQ4, the question regarding network relationships, we analyze music
sharing patterns, adopting a network-centric approach to elucidate the
underlying structures and associations that may characterize the distinctions
between depressed and non-depressed individuals. The core objective is to
ascertain whether there exists a notable clustering propensity among songs
shared by individuals with depression within distinct communities of music
playlists. Our analysis is based on the Million Spotify Playlist Dataset (Spo,
[n. d.]).

Playlists were pruned to retain only those songs that have a corresponding
presence in our pre-established music-sharing dataset. A playlist is
classified as a ‘depression playlist’ if a majority, i.e., over 50%, of its
comprising songs have been shared by individuals identified with depression on
Twitter. In the constructed network, nodes epitomize individual playlists.
Edges, on the other hand, are illustrative of the existence of shared songs
between pairs of playlists, with the weight of each edge being indicative of
the quantity of shared songs. We used community detection on this network to
find potential patterns and associations inherent to the songs shared by
individuals with depression.

Community detection analysis yielded distinct patterns. It unveiled diverse
degrees of dominance by depression and non-depression playlists, indicative of
heterogeneous user behaviors and music preferences. For Community detection we
used the Louvainalgorithm(Traag et al., 2019). 40 communities were detected in
total. Of these, 9 communities have more than 10 nodes each. Among the 5
communities were dominated by the depressed population, while 2 communities
were dominated by control. 2 communities showed almost equal amounts of
depressed and control individuals.

In the ensuing analysis, playlists associated with depression are represented
in green, while those unrelated to depression are depicted in pink.
Communities 1 and 4 are archetypal of clusters dominant in depression, as
illustrated in Figures 2(a) and 2(b). This observation is contrasted by
Community 5 (Figure 2(c)), which is characterized by a dominance of non-
depression playlists. Community 0, highlighted in Figure 2(d), presents a
balanced amalgamation of both depression and non-depression playlists.

The dichotomy in music sharing patterns, as evidenced through the network
analysis, provides valuable insights and augments our comprehensive
understanding of the behavioral and preferential disparities linked to
depression. Each community, delineated by distinct characteristics, affirms
the premise that music preferences and sharing behaviors are intrinsically
associated with the mental and emotional states of individuals. This
investigation not only amplifies the existing body of knowledge but also lays
a substantive foundation for subsequent research endeavours aiming at the
nexus of mental health, digital behaviors, and music preferences.

We additionally leveraged Latent Dirichlet Allocation (LDA) and community
detection to identify the prevailing topic within each community.
Intriguingly, communities 3 and 4, which are dominated by depression
playlists, were overwhelmingly governed by the Emotional Reflection topic.
Similarly, community 8, heavily associated with depression playlists, was
primarily dominated by Religious or Biblical Language topic. These insights
are consistent with preceding statistical analyses, indicating a higher
prevalence of Emotional Reflection and Religious or Biblical Language topics
within songs related to depression.

(a) Community 1

(b) Community 4

(c) Community 5

(d) Community 0

##  4\. Discussion

###  4.1. Summary of Findings

In this work, we analyzed the difference of music sharing between depression
and non-depression group from the perspectives of musical features, lyrics and
musical networks. Statistical analysis of musical features showed that there
are songs shared by depression are higher in energy, mode, acousticness,
instrumentalness, and duration, and lower in speechiness, time signature,
popularity and danceability. Lyric analysis showed that depression music
sharing have higher proportion in music recording, religious or biblical
language, everyday life, entertainment industry, recollection and creativity,
everyday experiences, emotional reflection, and everyday conversations topics,
yet lower proportion in explicit or RAP language, expressive language, musical
artists and collaborations, and personal relationships topics. Additionally,
individuals with depression may prefer songs with shorter and simpler
sentences, more assertive or confident language. In addition, Network analysis
revealed that depression playlists cluster together, suggesting a network
connection between depression songs. Emotional reflection and religious or
biblical language are dominant topics in depression playlist communities,
which are different from non-depression playlist communities. This explorative
analysis not only heightens our understanding of the nuanced relationships
between music preferences and mental health but also illuminates pathways for
cultivating sensitive, adaptive interventions and technologies in mental
health discourse and practice.

###  4.2. Implications

For RQ1, our work showed that several musical patterns differ between
depression and non-depression users. Our result is consistent with previous
research on music and psychology, showing that depression patient tend to
choose music with higher energy (Howlin and Rooney, 2021). Moreover, previous
research indicated that higher energy decrease the perception of relaxing for
songs (Baltazar and Västfjäll, 2020). However, as mental health research
suggests that relaxation can significantly reduce the level of depression (Jia
et al., 2020), it raises an intriguing paradox: while individuals with
depression are drawn to high-energy music, which might be less perceived as
relaxing, they may unintentionally be selecting music that doesn’t optimize
their psychological well-being, given the documented benefits of relaxation
for mitigating depressive symptoms. Moreover, our results found that songs
shared by depression individuals have lower popularity. Previous research on
depression also showed that preference for subculture elements (such as anime,
goths, punks) and less popular interests are positively associated with
depressive symptoms (Liu et al., 2022a; Bowes et al., 2015; Bešić and Kerr,
2009). This preference for less popular songs might stem from a variety of
factors: the depressive mood might be reinforced by subcultures (Brown and
Plener, 2017), and people with depression feel disconnected with popular
culture (Liu et al., 2022a). Additionally, reduced preference for speechiness
is coherent with the genres and lyrics analysis showing that songs shared by
depression users are less likely to be rap and contain explicit and rap
languages. This illustrates a consistent connection between the analysis from
different aspects in our work.

For RQ2, our work qualitatively showed that the artists and genres commonly
shared by depression and non-depression individuals are different. The result
shared common ground with previous work indicating that soul music
listening(hip hop and R&B) is a predictor of lower depression levels (Miranda
and Claes, 2008). Hip-hop and R&B, often associated with expressions of
resilience, empowerment, and an outlet for navigating through socio-emotional
challenges, might not resonate with depression individuals’ emotional and
psychological state.

For RQ3, our results demonstrated that the topic and language use in
depression and non-depression groups might be different. First, depression-
prone individuals are more likely to share songs with topics in everyday life
and emotional recollection. This is coherent with psychology research
indicating that those with depression report more rumination than the control
group (Olatunji et al., 2013). However, rumination can lead to negative
consequences in mood: rumination exacerbates and prolongs depressed mood
(Nolen-Hoeksema and Morrow, 1993), and rumination makes it harder for patients
to recover from psychological disorders (Watkins and Roberts, 2020; Morrison
and O’Connor, 2008). We hypothesize that listening to songs with emotional
recollection topic may induce ruminative thoughts, which, in turn, exacerbate
depression. Moreover, the LIWC result indicated that depressed individuals
prefer lyrics with short sentences. This is probably because depression
decreases the cognitive load (Nikolin et al., 2021). Therefore, short
sentences are easier to be perceived and liked by depressed individuals.

Finally, for RQ4, we found that most of the communities display polarized with
respect to either depression or non-depression playlists, which implies that
the depression playlists may share similar music preference and cluster
together. This may lead to the echo chamber effect, making depressed
individuals being suggested by more depression songs, and further exacerbate
and prolong depressed mood. Moreover, consistent with our topic modeling
result, our network analysis also showed that the dominant topics of
depression and non-depression communities are different. The dominance of
religious and biblical languages in depression community is coherent with both
LDA result and previous research showing that depressed individuals engaged
more with expression of religious involvement on social media (De Choudhury et
al., 2013a). In summary, our work validate that the music preference is
different between depression and non-depression group from both single song
and playlist level. Utilizing both LDA and community detection serves to
corroborate our findings and enhance the validity of the result.

We discern crucial implications across two pivotal dimensions. Firstly, the
characteristic features of individuals with depression in relation to their
music preferences can be observed. On a social level, this group demonstrates
a propensity for songs that may not be widely recognized or popular, possibly
reflecting a diminished sense of belongingness and reduced proclivity for
social engagement. This is also emphasized by their inclination away from soul
music, often imbued with themes of resilience, empowerment, and socialization.
From a cognitive perspective, a preference for shorter sentences may indicate
a desire for easily understood content, potentially deriving from a lower
cognitive load. Secondly, we delve into the consequences engendered by these
specific musical preferences. Individuals with depression tend to gravitate
towards high-energy music, paradoxically offering less relaxation, and exhibit
an avoidance of genres articulating resilience. Moreover, a preference for
songs with emotional reflection topics may inadvertently foster rumination,
further entrenching depressive states. This is not only evident in the lyric
analysis in the network analysis. Incorporating all aforementioned
observations, the musical preferences of depression individuals inadvertently
forge a self-reinforcing echo chamber, where the music perpetuates and
potentially exacerbates prevailing depressive moods and emotions. This echo
chamber effect is manifested not merely through individual listening choices
but is further perpetuated by the recommendation algorithms that may suggest
additional depression playlists to listeners. Thus, recommendation algorithms,
influenced by these patterns, may unwittingly suggest additional depression-
congruent content to listeners, thereby sustaining and potentially
intensifying the resonance with depressive moods and emotions through
continued exposure to thematically similar music.

Based on the result, we also suggest that music application develop algorithms
or frameworks to gauge the emotional state of listeners, by analyzing their
music listening patterns over time, thus providing an unintrusive way to
monitor emotional well-being. Music application can inquire the mental health
condition of their users and recommend songs or playlists with less depressive
features, and explore the efficacy of such tailored musical interventions and
whether guiding listening habits can serve as a viable strategy in emotional
self-regulation or mental health management. In addition, the answer to the
question ”How can recommendation algorithms avoid negative reinforcement and
echo chamber effect?”, needs to be explored. These ideas may help developing
holistic mental health supporting systems.

###  4.3. Limitation and Future Work

We acknowledge our study has several limitations, some of which point to
intriguing future research directions. First, this study focused on the
overall profile and preferences of depressed and non depressed users, while a
potential future work can be eliminating the overlapping features and
analyzing the distinctive unique features for each of the users classes.
Second, since we do not have the playlist information from depression/non-
depression users, we cannot analyze the actual music preference connection
between depressed individuals in the network analysis. Instead, we have to
analyze in an indirect way that regards playlists with a majority of songs
shared by depressed individuals as depression playlists. A potential future
direction could focus on collecting social network data with depression
playlists to further study this question.

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