# Modelling Moral Traits with Music Listening Preferences and Demographics

Vjosa Preniqi
Centre for Digital Music
Queen Mary University of London
<EMAIL_ADDRESS>
Kyriaki Kalimeri
ISI Foundation
<EMAIL_ADDRESS>
Charalampos Saitis
Centre for Digital Music
Queen Mary University of London
<EMAIL_ADDRESS>

###### Abstract

Music is an essential component in our everyday lives and experiences, as it
is a way that we use to express our feelings, emotions and cultures. In this
study, we explore the association between music genre preferences,
demographics and moral values by exploring self-reported data from an online
survey administered in Canada. Participants filled in the moral foundations
questionnaire, while they also provided their basic demographic information,
and music preferences. Here, we predict the moral values of the participants
inferring on their musical preferences employing classification and regression
techniques. We also explored the predictive power of features estimated from
factor analysis on the music genres, as well as the generalist/specialist (GS)
score for revealing the diversity of musical choices for each user. Our
results show the importance of music in predicting a person’s moral values
(.55-.69 AUROC); while knowledge of basic demographic features such as age and
gender is enough to increase the performance (.58-.71 AUROC).

##  1 Introduction

Music influences a wide range of behaviours including cognitive performance
such as thinking, reasoning, problem-solving, creativity, and mental
flexibility [26], while it is proven to have therapeutic effects in various
medical situations [20]. Scientists showed that music is a fundamental factor
in the evolution of humans and societies, and it is tightly related to social
connection, communication and development [7, 21]. Music is shown to improve
concentration while working, boost the stimulation during the fitness workout,
regulate the moods, and it helps to relieve stress and anxiety [30, 22]. A
notable influence on increasing rehabilitation rates after surgery [15], and
capturing mental health problems such as depression, autism, post-traumatic
disorders and so on [5, 10] was also associated with music.

More recently, evidence that music listening behaviours reflect on people’s
personality traits and values were provided analysing on a variety of data
including self-reported surveys [8, 9, 13], online music streaming [2, 19],
and social media [29]. Findings from surveys have showed that musical taste is
strongly related to personal values [9] and political orientation [8] and that
personality can predict musical sophistication even after controlling for
demographic variables and musicianship [13]. Using data from the popular but
controversial myPersonality Facebook application, Nave et al. [29] found that
both people’s reactions to unfamiliar music samples and “likes” for music
artists predicted personality traits. A study on the social music platform
Last.fm showed that the music listening behaviours of users can predict their
demographics, including age, gender, and nationality [19]. Anderson et al. [2]
presented strong evidence about the connection between personalities and music
listening preferences by utilising Spotify music streaming data. Such
knowledge has the potential to not only better our understanding of the social
psychology of music, but also to further support the improvement of
personalised content and enhance data-driven decision-making in music
marketing and entertainment.

Building on comparable interactionist theories, here we set to explore the
less attended relation between moral values and music preferences. We
operationalise morality according to the Moral Foundations Theory (MFT)[12],
which defines five dimensions of morality, namely Care/Harm,
Fairness/Cheating, Loyalty/Betrayal, Authority/Subversion, and
Purity/Degradation. These can further collapse into two superior moral
foundations: of Individualising, compounded by fairness and care, that asserts
that the basic constructs of society are the individuals and hence focuses on
their protection and fair treatment, and of Binding, that summarises purity,
authority and loyalty and is based on the respect of leadership and
traditions. Moral foundations are considered to be higher psychological
constructs than the more commonly investigated personality traits [27] yet the
moral values have attracted less attention from music scientists.

In the computational social science field, moral foundations have been studied
only recently. Kim and colleagues [18] observed digital traces aggregated from
repeated game play data in order and pointed out several associations with
people’s values and moral identity. Kalimeri et al. [17] tried to predict
moral foundations from digital behaviour traits collected from users’
smartphones and browser activities. Their results showed that moral traits and
values are more complex, and thus harder to predict compared to demographics
which can be more accurately inferred from passively collected data.
Nevertheless, these studies provide a realistic dimension of the possibilities
of modelling moral traits for delivering better targeted and more effective
interventions. Indeed, recent work has indicated that negative emotions
enforced by types of music can worsen moral judgement [3] although that study
did not rely on a psychometrically validated tool like the MFT.

##  2 Data Collection and Feature Engineering

Here, we employ data from a survey administered online for a general scope
marketing project. The survey consists of approximately 2,000 participants
(51% females) from 12 different regions in Canada. The participants filled in,
among other items, information about their demographics, including age,
gender, education, and political views. They completed the validated Moral
Foundations questionnaire [14], while stated their preferences on 13 music
genres (on a 5-point Likert scale where 1 = strongly dislike and 5 = strongly
like). The considered music genres were: alternative pop/rock, christian,
classical, country, folk, heavy metal, rap/hip-hop, jazz, latin, pop, punk,
R&B, and rock. After an initial pre-processing step, during which we removed
participants who failed in the catch items of the survey, we remained with
1,062 participants (55% females). Table 1 summarises the major demographic
features of our dataset.

In addition to using the ratings on each of the 13 music genres, we considered
two steps of dimensionality reduction as part of feature engineering. First,
to reveal the underlying structure of the 13 genre preferences, we performed
an exploratory factor analysis using principal axis factoring with promax
rotation. A 5-factor solution was retained, which explained 67% of total data
variance: {jazz, classical, latin}, {punk, heavy metal, rap/hip-hop}, {pop,
R&B}, {country, christian, folk}, and {rock, alternative pop/rock} (genres
ordered by decreasing factor loading). Similar factors have been reported for
Greek listeners [9]. Second, to quantify the musical diversity of a given
respondent, the loadings of each of the 13 music genres onto the five factors
were considered as that genre’s vector representation in the “preference
space” and were used to compute a type of generalist-specialist (GS) score,
inspired by the work of Anderson et al. [1]. Intuitively, generalists versus
specialists will have genre vectors spread apart versus close together in the
preference space. We first calculate the user centroid
$\overrightarrow{ct_{i}}$ of the genre vectors representing the loadings of
the genre on the five factors $\overrightarrow{l_{j}}$, weighted by the number
of genre scores rated by each respondent $w_{j}$:

$\overrightarrow{ct_{i}}=\frac{1}{\sum{w_{j}}}\cdot\sum{w_{j}\overrightarrow{l_{j}}}\;.$
(1)

The GS score is then the average cosine similarity between a genre vector and
the preference-weighted average of a participants’ genre vectors:

$GS(u_{i})=\frac{1}{\sum{w_{j}}}\cdot\sum
w_{j}\frac{\overrightarrow{l_{j}}\cdot\overrightarrow{ct_{i}}}{\parallel{\overrightarrow{l_{j}}\parallel}\cdot\parallel{\overrightarrow{ct_{i}}}\parallel}\;.$
(2)

Table 1: The summary of our dataset (cleaned) with major demographic attributes utilised for this research work. These labels were used as predictors for MFT categories. Attributes |  Demographics |  Sample size (N = 1062)
---|---|---
Age |  18-24 |  80 (7.5%)
|  25-34 |  154 (14.5%)
|  35-44 |  205 (19.3%)
|  45-54 |  205 (21.9%)
|  55-64 |  187 (17.6%)
|  65+ |  203 (19.1%)
Gender |  Female |  588 (55.3%)
|  Male |  474 (44.6%)
Education |  Less than High School |  35 (3.2%)
|  High school graduate |  195 (18.3%)
|  Some College |  154 (14.5%)
|  Trade or professional school |  115 (10.8%)
|  College Graduate |  349 (32.8%)
|  Post Graduate work or degree |  205 (19.3%)
Political Party |  Conservative |  328 (30.8%)
|  Liberal |  279 (26.2%)
|  New Democratic Party |  184 (17.3%)
|  Green Party |  66 (6.2%)
|  Party Quebecois |  56 (5.2%)
|  I don’t vote |  149 (14%)

##  3 Experiments and Results

###  3.1 Exploratory Analysis

We explored the extent to which musical genres’ preferences, demographics,
political views and moral traits are correlated to each-other. We observed a
positive Spearman correlation of age with christian, classical, country and
folk music genres ($\rho_{s}=\\{0.18$, 0.21, 0.20, 0.25}), respectively. On
the other side, heavy metal, hip-hop/rap and punk were more preferred by
younger ages, whereas older people expressed their dislike towards these
genres ($\rho_{s}=\\{$-$0.22$, $-$0.38, $-$0.38 }).

Education was positively related with classical music, jazz and latin music
($\rho_{s}=\\{0.22$, 0.13, 0.13}), indicating that people with higher
education preferred these genres. With respect to morals, loyalty, authority
and purity were positively correlated with christian music
($\rho_{s}=\\{0.18$, 26, 38}) and country music ($\rho_{s}=\\{0.17$, 20, 21}).
Negative correlations were observed between purity and more rebellious and
non-mainstream genres such as alternative pop/rock, heavy metal, punk and rock
($\rho_{s}=\\{$-$0.16$, $-$0.14, $-$0.21, $-$0.16 }). Looking at the political
views of the respondents, conservatives were positively correlated with
christian and country ($\rho_{s}=\\{0.12$, 0.12}) and negatively correlated to
hip-hop/rap and punk ($\rho_{s}=\\{$-$0.17$, $-$0.15}).

Correlating the GS score with moral foundations we observed a moderate
negative correlation of the GS score and Purity, ($\rho_{s}=$-$0.17$). Purity
foundation is shaped by the psychology of disgust and contamination; hence,
likely the participants who score high on this foundation, are translating
these notions to the music domain, listening only one or few music genres,
avoiding exposure to multiple - less “noble” - music genres.

###  3.2 Moral Values Classification

Our main research question is whether we can predict peoples’ moral values
from their music preferences. To answer this question, we postulate the task
as a supervised classification one, developing a series of experiments to
assess the predictive power of different variables (see Table 2).

We assign the class label “high” to individuals with moral scores higher than
the population median for the specific foundation, and “low”, otherwise. We
perform 5-fold cross-validation on shuffled data (to avoid dependencies in
successive data points), with 70% of training and 30% testing data. We opt for
the gradient boosting algorithm XGBoost (XGB) [6]. To take into account the
effect of unbalanced class labels in the performance metric, we evaluate our
models with the area under the receiver operating characteristic (AUROC)
metric. The best model is then chosen as the one that maximized the weighted
area under receiver operating characteristic (AUROC) statistic.

Table 2: Detailed list of the experiments we performed with the list of features employed as predictors in each one of them. Experiment ID |  Features Employed as Predictors
---|---
EX1 |  13 Music Genres
EX2 |  5 factors
EX3 |  GS score
EX4 |  13 Music Genres, Age, Gender
EX5 |  13 Music Genres, Age, Gender, Education
EX6 |  13 Music Genres, Age, Gender, Education, Political Views
Table 3: Moral traits classification with XGBoost, average weighted AUROC and
standard deviation over 5-fold cross-validation (baseline is .50). Music
Preference - Classification Task
---
|  EX1 |  EX2 |  EX3
Care |  .57 (3.7) |  .54 (2.1) |  .52 (1.5)
Fairness |  .56 (2.9) |  .52 (1.1) |  .48 (2.7)
Authority |  .63 (0.8) |  .60 (1.1) |  .49 (1.7)
Purity |  .69 (2.8) |  .65 (3.0) |  .57 (2.3)
Loyalty |  .61 (2.4) |  .56 (1.9) |  .48 (3.1)
Individ. |  .55 (3.5) |  .51 (0.8) |  .50 (1.6)
Binding |  .67 (2.4) |  .63 (2.2) |  .52 (1.9)

Initially, we compared the predictive power of the genre information against
the features engineered by us (EX1, EX2, and EX3). We trained one model per
moral foundation, and we present the cross validated results in Table 3. We
notice that the information obtained directly about the music preferences
(EX1) outperforms the features we developed. When comparing the scenarios, we
observe that the five factors, and the GS score accounting only for part of
the variance in the data, did not manage to outperform the explicit
information on music preferences.

A question that emerges naturally, is whether including knowledge regarding
the participants’ basic demographic features (i.e. age, political views,
education level) will improve the prediction of their moral values. Table 4
summarises the results when age, gender, education and political views are
incorporated in the design. As expected, the more information we have about
the participants the more precise our predictions become, however, the
improvement is minimum. This shows us the importance of music behaviours alone
in explaining the variability of our moral values.

Further, we employed SHAP (SHapley Additive exPlanations), a game theory
approach developed to explain the contribution of each feature to the final
output of any machine learning model. SHAP values provide both global and
local interpretability, meaning that we can assess both how much each
predictor and each observation, respectively, contribute to the performance of
the classifier. The local explanations are based on assigning a numerical
measure of credit to each input feature. Then, global model insights can be
obtained by combining many local explanations from the samples [24]. As
mentioned by the authors, the classic Shapley values can be considered
“optimal” in the sense that within a large class of approaches, they are the
only way to measure feature importance while maintaining several natural
properties from cooperative game theory [23]. SHAP’s output helps to
understand the general behaviour of our model by assessing the impact of each
input feature in the final decision, thus enhancing the usefulness of our
framework.

Figure 1 depicts the impact of each feature in the prediction of the two
superior foundations, when including demographics, education level, and
political views, as well as the 13 music genres (EX6 in Table 4). Across all
experimental designs, the christian music genre emerged as the most predictive
one. Regarding the demographics, the participants’ age stands out both for
Individualising and Social Binding foundations.

Figure 1: Feature contributions (via SHAP values). The higher the SHAP value
(x-axis) the more the feature contributes to the moral prediction. Horizontal
location of the red and blue points reveal whether the value is linked with
higher or lower predictions of each moral traits. Whereas the colours (red and
blue) show if that variable is high or low for that observation. For example,
red points positioned in the left of x-axis, disclose that the variable is
negatively correlated with the predicted feature (i.e., Conservatives with
Individualising, etc)

.

Table 4: Moral traits classification with XGBoost for different number of
predictors (EX1, EX4, EX5, EX6). Models evaluated based on average weighted
AUROC and standard deviation over 5-fold cross-validation (baseline is 50).
Moral Traits Predictors
---
|  EX1 |  EX4 |  EX5 |  EX6
Care |  .57 (3.7) |  .62 (3.2) |  .62 (3.0) |  .63 (2.3)
Fairness |  .56 (2.9) |  .58 (2.5) |  .57 (2.3) |  .62 (4.3)
Authority |  .63 (0.8) |  .64 (1.6) |  .65 (2.0) |  .66 (1.6)
Purity |  .69 (2.8) |  .71 (3.0) |  .71 (1.4) |  .71 (1.6)
Loyalty |  .61 (2.4) |  .67 (3.5) |  .66 (2.2) |  .66 (2.9)
Individ. |  .55 (3.5) |  .59 (2.4) |  .59 (3.3) |  .61 (1.8)
Binding |  .67 (2.4) |  .71 (3.2) |  .70 (2.2) |  .72 (2.9)

###  3.3 Regression Task

Data binning is a common way to aggregate information and facilitate the
classification tasks. However, theare are known issues to dichotomisation of
variables which often lead to misleading results [25]. Here, to ensure that
the most predictive features emerged from the classification process, are
indeed descriptive of the moral foundation we conducted a regression analysis.
At this point, the aim is to understand whether we can estimate the original
moral scores (predicting the quantity) based on our explanatory variables in
disposition (i.e. music genres ad demographics).

To do so, we trained an XGBoost Regressor for each moral foundation. We
maintained the same experimental designs and settings as in the classification
task (data shuffling, 5-fold cross-validation scheme, with 70% of the data
points used for training and the 30% for testing). For model evaluation, we
used Mean Absolute Error (MAE). These options allow for a direct comparison of
the most predictive features with the ones emerged from the classification
task.

Table 5: Moral traits regression models for the experimental designs EX1, EX2,
and EX3, with XGBoost Regression algorithm. Mean Absolute Error (MAE) and
standard deviation over 5-fold cross validation was used for the evaluation of
each model. Each value on the table measured by MAE represents the average of
the absolute error between predicted and actual values, thus, lower MAE scores
represent better model performance Music Preference - Regression Task
---
|  EX1 |  EX2 |  EX3
Care |  3.86 (13.2) |  3.72 (10.9) |  3.89 (7.0)
Fairness |  3.27 (11.1) |  3.28 (9.6) |  3.55 (8.7)
Authority |  4.19 (23.3) |  4.20 (16.7) |  4.47 (13.9)
Purity |  4.86 (19.7) |  4.99 (25.0) |  5.35 (21.0)
Loyalty |  4.46 (12.1) |  4.33 (19.4) |  4.64(11.6)
Individ. |  3.23 (9.5) |  3.17 (8.5) |  3.35 (9.9)
Binding |  3.86 (15.1) |  3.79 (6.3) |  4.22 (18.5)

Table 5, summarises the results for the regression models. We notice that as
in the classification approach, when adding information to the models the MAE
decreases indicating that the model fits the data better. Also in this case
the gain of adding more information is relatively small with respect to the
music genres alone.

We visualise the most predictive features employing again the Shap values (see
Figure 2). Interestingly, the christian music genre appears again as the most
important predictor for both the Binding and Individualising foundations while
age is also very important for both models. Generally, the feature importances
for the output of the XGboost regressor, is in line with the feature
significance obtained with the classification approach. The same holds for all
the moral foundations which are not depicted here for spacing issues.

Figure 2: Features with the most impact on the XGBoost regression model
output. Horizontal location of the red and blue points reveal whether the
value is linked with higher or lower predictions of each moral traits. Whereas
the colours (red and blue) show if that variable is high or low for that
observation. For example, red points positioned in the left of x-axis,
disclose that the variable is negatively correlated with the predicted feature
(i.e., punk with MFT Binding, etc). Table 6: Moral traits classification with
XGBoost for different number of regressors (EX1, EX4, EX5, EX6). Mean Absolute
Error (MAE) and standard deviation over 5-fold cross validation was used for
the evaluation of each model. Moral Traits Regressors
---
|  EX1 |  EX4 |  EX5 |  EX6
Care |  3.86 (13.2) |  3.72 (6.2) |  3.71 (9.3) |  3.60 (8.0)
Fairness |  3.27 (11.1) |  3.25 (8.2) |  3.19 (10.5) |  3.12 (13.4)
Authority |  4.19 (23.3) |  4.14 (15.9) |  4.10 (9.3) |  4.09 (11.0)
Purity |  4.86 (19.7) |  4.86 (20.4) |  4.74 (18.9) |  4.71 (15.8)
Loyalty |  4.46 (12.1) |  4.19 (22.8) |  4.20 (18.7) |  4.21 (18.7)
Individ. |  3.23 (9.5) |  3.17 (14.4) |  3.17 (8.0) |  3.0 (8.9)
Binding |  3.86 (15.1) |  3.80 (11.0) |  3.76 (13.1) |  3.74 (5.4)

##  4 Conclusion

Henry Wadsworth Longfellow wrote, “Music is the universal language of
mankind.” Contemporary research has found converging evidence that people
listen to music that reflects their psychological traits and needs and help
express emotions, cultures, values and personalities.

In this paper, we analysed the less explored links between musical
preferences, demographics (age, gender, political views, and education level)
and Moral Foundations (MFT [12]). We applied both classification and
regression models for moral traits prediction. From classification results, it
was inferred that MFT Binding (encompassing purity, authority and loyalty) was
best predicted with AUROC score 72%, whereas MFT individualising (including
care and fairness) showed weaker results with AUROC score 61%. While, for the
regression task the lowest MAE was 3.0 for the Individualising and 3.74 for
Social Binding. In both approaches, the most impactful features on inferring
morality were christian music and age.

Moral foundations are strongly tied to political views; despite that, the
musical features are more predictive than political leanings. Social binding
is related to conservative political views [11] \- and in fact is predicted by
christian, and country music. We notice that people instinctually express
their moral values through the music they listen to. We instinctively
categorize objects, symbols, but also people, creating a notion of social
identity. According to the social identity theory members of a group will seek
to find negative aspects to other groups thus enhancing their self-image [31].
Such reasoning reflects on a broad range of attitudes related to stereotype
formations [28] but also as we notice here to musical preferences. For
instance, people higher in social binding foundations (authority, loyalty, and
purity) tend to listen to country music which often expresses notions of
patriotism. Christian music is also a predictor of this superior foundation,
which again fosters the notion of belonging to a group. On the other hand,
music genres such as punk, and hip hop are know to challenge the traditional
values and the status quo, hence are preferred by people who strongly value
these aspects.

To conclude, our findings suggested that musical preferences are quite
informative of deeper psychological attributes; still there is space for
improvement. For instance, we noticed that the care, fairness, and loyalty
foundations are very hard to predict; to this extend we aim to explore musical
content analysis; incorporating linguistic cues as well as the moral valence
scores proposed by Araque et al. [4] on lyrics will improve the performance.

Generally, self-reported preferences for music genres reflect, at least
partially, preferences for external properties of music. We aim to further
investigate the association between music listening preferences and moral
traits and the link of music with other psychological aspects such as human
values and emotions. In future work we aim to delve deeper into the relation
between music and morality, and between music and other universal human
values, by using passively collected digital traits of music listening
behaviours outside a laboratory setting and over a period of time [2].
Developing data-informed models will help unlock the potential of
personalised, uniquely tailored digital music experiences and communication
strategies [16, 1]. Predicting the moral values from listening behaviours can
provide noninvasive insights on the values or other psychological aspects of
populations at a large scale.

## References

  * [1] Ashton Anderson, Lucas Maystre, Ian Anderson, Rishabh Mehrotra, and Mounia Lalmas.  Algorithmic effects on the diversity of consumption on spotify.  In Proceedings of The Web Conference 2020, pages 2155–2165, 2020\.
  * [2] Ian Anderson, Santiago Gil, Clay Gibson, Scott Wolf, Will Shapiro, Oguz Semerci, and David M Greenberg.  “just the way you are”: Linking music listening on spotify and personality.  Social Psychological and Personality Science, page 1948550620923228, 2020.
  * [3] Alessandro Ansani, Francesca D’Errico, and Isabella Poggi.  ‘you will be judged by the music i hear’: A study on the influence of music on moral judgement.  In Web Intelligence, volume 17, pages 53–62. IOS Press, 2019.
  * [4] Oscar Araque, Lorenzo Gatti, and Kyriaki Kalimeri.  Moralstrength: Exploiting a moral lexicon and embedding similarity for moral foundations prediction.  Knowledge-based systems, 191:105184, 2020.
  * [5] Catherine Carr, Patricia d’Ardenne, Ann Sloboda, Carleen Scott, Duolao Wang, and Stefan Priebe.  Group music therapy for patients with persistent post-traumatic stress disorder–an exploratory randomized controlled trial with mixed methods evaluation.  Psychology and Psychotherapy: Theory, Research and Practice, 85(2):179–202, 2012.
  * [6] Tianqi Chen and Carlos Guestrin.  Xgboost: A scalable tree boosting system.  In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785–794, 2016.
  * [7] Ian Cross and Iain Morley.  The evolution of music: Theories, definitions and the nature of the evidence.  Communicative musicality: Exploring the basis of human companionship, pages 61–81, 2009.
  * [8] Scott P Devenport and Adrian C North.  Predicting musical taste: Relationships with personality aspects and political orientation.  Psychology of Music, 47(6):834–847, 2019.
  * [9] Antonis Gardikiotis and Alexandros Baltzis.  ‘rock music for myself and justice to the world!’: Musical identity, values, and music preferences.  Psychology of Music, 40(2):143–163, 2012.
  * [10] Christian Gold, Hans Petter Solli, Viggo Krüger, and Stein Atle Lie.  Dose–response relationship in music therapy for people with serious mental disorders: Systematic review and meta-analysis.  Clinical psychology review, 29(3):193–207, 2009.
  * [11] Jesse Graham, Jonathan Haidt, and Brian A Nosek.  Liberals and conservatives rely on different sets of moral foundations.  Journal of personality and social psychology, 96(5):1029, 2009.
  * [12] Jesse Graham, Brian Nosek, Jonathan Haidt, Ravi Iyer, Spassena Koleva, and Peter H Ditto.  Mapping the moral domain.  Journal of personality and social psychology, 101(2):366–85, August 2011.
  * [13] David M Greenberg, Daniel Müllensiefen, Michael E Lamb, and Peter J Rentfrow.  Personality predicts musical sophistication.  Journal of Research in Personality, 58:154–158, 2015.
  * [14] Jonathan Haidt and Craig Joseph.  Intuitive ethics: How innately prepared intuitions generate culturally variable virtues.  Daedalus, 133(4):55–66, 2004.
  * [15] Jenny Hole, Martin Hirsch, Elizabeth Ball, and Catherine Meads.  Music as an aid for postoperative recovery in adults: a systematic review and meta-analysis.  The Lancet, 386(10004):1659–1671, 2015.
  * [16] Kyriaki Kalimeri, Mariano G Beiró, Matteo Delfino, Robert Raleigh, and Ciro Cattuto.  Predicting demographics, moral foundations, and human values from digital behaviours.  Computers in Human Behavior, 92:428–445, 2019.
  * [17] Kyriaki Kalimeri, Mariano G. Beiró, Alessandra Urbinati, Andrea Bonanomi, Alessandro Rosina, and Ciro Cattuto.  Human values and attitudes towards vaccination in social media.  In Companion Proceedings of The 2019 World Wide Web Conference, pages 248–254, 2019.
  * [18] Eunkyung Kim, Ravi Iyer, Jesse Graham, Yu-Han Chang, and Rajiv Maheswaran.  Moral values from simple game play.  In International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, pages 56–64. Springer, 2013.
  * [19] Thomas Krismayer, Markus Schedl, Peter Knees, and Rick Rabiser.  Predicting user demographics from music listening information.  Multimedia Tools and Applications, 78(3):2897–2920, 2019.
  * [20] Shuai-Ting Lin, Pinchen Yang, Chien-Yu Lai, Yu-Yun Su, Yi-Chun Yeh, Mei-Feng Huang, and Cheng-Chung Chen.  Mental health implications of music: Insight from neuroscientific and clinical studies.  Harvard review of psychiatry, 19(1):34–46, 2011.
  * [21] Chris Loersch and Nathan L Arbuckle.  Unraveling the mystery of music: Music as an evolved group process.  Journal of Personality and Social Psychology, 105(5):777, 2013.
  * [22] Adam J Lonsdale and Adrian C North.  Why do we listen to music? a uses and gratifications analysis.  British journal of psychology, 102(1):108–134, 2011.
  * [23] Scott Lundberg and Su-In Lee.  A unified approach to interpreting model predictions.  arXiv preprint arXiv:1705.07874, 2017.
  * [24] Scott M. Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M. Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee.  Explainable ai for trees: From local explanations to global understanding, 2019.
  * [25] Robert C MacCallum, Shaobo Zhang, Kristopher J Preacher, and Derek D Rucker.  On the practice of dichotomization of quantitative variables.  Psychological methods, 7(1):19, 2002.
  * [26] Raymond MacDonald, Gunter Kreutz, and Laura Mitchell.  Music, health, and wellbeing.  Oxford University Press, 2013.
  * [27] Dan P McAdams and Jennifer L Pals.  A new big five: fundamental principles for an integrative science of personality.  American psychologist, 61(3):204, 2006.
  * [28] Saul L Miller, Jon K Maner, and D Vaughn Becker.  Self-protective biases in group categorization: Threat cues shape the psychological boundary between “us” and “them”.  Journal of personality and social psychology, 99(1):62, 2010.
  * [29] Gideon Nave, Juri Minxha, David M Greenberg, Michal Kosinski, David Stillwell, and Jason Rentfrow.  Musical preferences predict personality: evidence from active listening and facebook likes.  Psychological Science, 29(7):1145–1158, 2018.
  * [30] Adrian C North, David J Hargreaves, and Jon J Hargreaves.  Uses of music in everyday life.  Music perception, 22(1):41–77, 2004.
  * [31] Henri Tajfel, John C Turner, William G Austin, and Stephen Worchel.  An integrative theory of intergroup conflict.  Organizational identity: A reader, 56(65):9780203505984–16, 1979\.