=Paper=
{{Paper
|id=Vol-3866/paper1
|storemode=property
|title=Music and Myth: The Relationship Between Music Preference and Unverified Beliefs
|pdfUrl=https://ceur-ws.org/Vol-3866/short1.pdf
|volume=Vol-3866
|authors=Elena Spirova,Arsen Matej Golubovikj,Marko Tkalčič
|dblpUrl=https://dblp.org/rec/conf/hci-si/SpirovaGT24
}}
==Music and Myth: The Relationship Between Music Preference and Unverified Beliefs==
https://ceur-ws.org/Vol-3866/short1.pdf
Music and Myth: The Relationship Between Music
Preference and Unverified Beliefs
Elena Spirova1 , Arsen Matej Golubovikj1 and Marko Tkalčič1
1
University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies, Glagoljaška ulica 8,
6000 Koper, Slovenija
Abstract
This study investigates the relationship between music preferences and unverified beliefs, such as
beliefs in the paranormal, pseudoscience, and conspiracy theories. We explore two key questions: (i) Is
there a correlation between music preferences and belief constructs? (ii) Can these beliefs be predicted
from music preferences? Through a survey of 62 participants, we gathered data on music preferences,
personality traits (using the Big Five), and unverified beliefs (using the Australian Sheep-Goat Scale,
Revised Paranormal Belief Scale, and Belief in Conspiracy Theories Inventory). Utilizing this survey,
we performed correlational and predictive analysis to address our research questions. Although music
preferences alone did not prove strong predictors of unverified beliefs, this study highlights the need
for further exploration of the relationship between digital behaviour and belief systems, providing a
foundation for future research.
Keywords
music preferences, beliefs, personality, personalization, music recommendation
1. Introduction
Since ancient times, humans have worshipped gods, feared witches, and been wary of the
unknown. Today, beliefs in astrology, unlucky numbers, and government distrust persist. What
sets believers apart from sceptics? Is it merely their beliefs, or is there a deeper distinction? Are
these beliefs also manifested in digital behaviour? Can we use these digital behaviour traces to
predict beliefs? In this work, we explore if music preferences, which could be extracted from
digital behaviour traces on music streaming platforms, can predict an individual’s inclination
towards paranormal, pseudo-scientific, and conspiracy theories. This work follows two main
research questions:
• RQ1: Is there a correlation between user music preferences and belief constructs?
• RQ2: Can we successfully predict the belief constructs from music preferences of users
and their respective song characteristics?
HCI SI 2024: Human-Computer Interaction Slovenia 2024, November 8th, 2024, Ljubljana, Slovenia
Envelope-Open 89242102@student.upr.si (E. Spirova); matej.golubovikj@famnit.upr.si (A. M. Golubovikj);
marko.tkalcic@famnit.upr.si (M. Tkalčič)
GLOBE https://markotkalcic.com/ (M. Tkalčič)
Orcid 0009-0002-0378-400X (A. M. Golubovikj); 0000-0002-0831-5512 (M. Tkalčič)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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2. Related Work
Previous research has established a strong link between personality traits and preferences.
Ferwerda et al. [1] demonstrated that personality, particularly the Big Five traits, can effectively
predict music tastes across different age groups. Their findings revealed that musical preferences
evolve over time, with individuals tending to narrow their variety of preferred genres. Motamedi
et al. [2] investigated the potential for predicting personality traits through movie preferences,
exploring eudaimonic and hedonic orientations in film viewers. Preniqi et al. [3] explored the
relationship between music preferences and moral values, using both lyrical and audio analysis.
They found that music can offer insights into an individual’s moral framework. Our study differs
from this body of literature as it includes a third dimension: unverified beliefs. In other words,
our work will explore whether a transitive relationship exists: musical preferences, personality
and beliefs. Personality and beliefs can be intricately connected (for example, Andersson et al.
[4] finds a link between narcissism and belief in astrology), and analysis in this direction can
deepen our understanding of musical preferences.
In the context of unverified beliefs, Drinkwater et al. [5] validated the Australian Sheep-Goat
Scale (ASGS) [6] as a reliable measure of belief in the paranormal, a scale also employed in our
study. Similarly, Tobacyk [7] introduced the Revised Paranormal Belief Scale (R-PBS), refining
the original 1980 scale to enhance its accuracy in measuring paranormal beliefs. Both the ASGS
and R-PBS are critical components of our work. Additionally, Lantian et al. [8] developed a
brief yet effective scale for measuring conspiracy thinking, which we also use in our research.
Lastly, in the domain of personality, Rammstedt and John [9] validated the shortened Big Five
scale, emphasizing the benefits of reduced participant fatigue and increased engagement.
This body of work lays the foundation for our investigation into the triadic relationship
between music preferences, personality traits, and beliefs, expanding the scope of existing
research by integrating these elements into a unified model. To the best of our knowledge,
no previous work has explored the relationship between musical preferences, personality and
unverified beliefs in a single study
3. Methodology
3.1. Data Collection
To our knowledge, there were no existing datasets that would be suitable for our research.
We therefore performed a survey of 62 individuals (eligible after data cleaning) through a
questionnaire. The questionnaire elicited the participants’ (i) music preferences, (ii) personality
and (iii) unverified beliefs (beliefs in paranormal phenomena, pseudoscience and conspiracy
theories). We now present the contents of the survey in more detail.
Music preferences. We collected the participants’ music preferences through song ratings.
Participants were asked to rate 70 randomized songs on a 5-point scale. The songs were selected
from a Spotify playlist with 350 songs, contributed to by 49 individuals1 , each adding 5-10 songs
1
These 49 individuals were sampled from the same population as the participants in our survey. The rationale for
selecting songs using this method was to ensure a higher likelihood that the songs included in our survey would be
(i) familiar to our participants and (ii) resonate with their personal identities.
that resonated with their character. Before inclusion in the survey, the songs were categorized
using twelve common themes identified by Henard and Rossetti [10], based on Billboard hits
from 1960 to 2009. To ensure balance, we excluded categories with fewer than five songs, leaving
ten themes (detailed in Section 3.2). A Python script using GPT-3.5-turbo assigned these themes
to the songs. The final songs were then randomized into five groups of seventy, resulting in
five survey versions for maximum ratings. Each of these five survey versions was administered
to a different set of participants.
Personality. To elicit the participants’ personality, we used a shortened version of the Big
5 personality test [9]. The test consisted of 10 questions, on a 5-point (Likert) scale, gauging
the participant’s personality using the five-factor model [11]: (i) Agreeableness, (ii) Openness
to (new) experience, (iii) Conscientiousness, (iv) Extraversion and (v) Neuroticism. We used a
shortened version of the test to reduce participant fatigue during the survey.
Unverified beliefs. We used three tests to derive the participants’ inclination towards
unverified beliefs: (i) the Australian Sheep-Goat Scale (ASGS) [5], (ii) the Revised Paranormal
Belief Scale [7] and (iii) the (Shortened) Belief in Conspiracy Theories Inventory [8]. The
Australian Sheep-Goat Scale (ASGS) consists of 18 true/false questions giving a score from
0 to 36 on the participant’s belief in pseudo-science and the paranormal [7]. The Revised
Paranormal Belief Scale (RPBS) ascertains the level of the participant’s belief, on a scale from 1
to 7, in (i) Traditional Religious Belief, (ii) Psi (belief in psychic powers, telepathy, clairvoyance,
etc.), (iii) Witchcraft, (iv) Superstition, (v) Spiritualism, (vi) Extraordinary Life Forms and (vii)
Precognition (belief in extrasensory perception, magical prediction of the near future). We
excluded the questions for (i) Witchcraft, and (ii) Extraordinary Life Forms from the study
(in total 19 questions were used from this test). Finally, the (Shortened) Belief in Conspiracy
Theories Inventory (BCTI) measures the participant’s inclination towards conspiracy thinking
as a value from 1 to 7 using 5 questions on the same scale (each eliciting the degree of belief in
a conspiratory statement, eg. the US government is spying on us).
In addition, the participants were asked to provide their age and gender (demographic
information).
The survey was administered through the 1ka platform. It took 15–20 minutes to complete,
with voluntary participation from individuals aged 18 and older. When the survey was concluded
users with fewer than 10 song ratings were excluded, leaving us with 62 eligible responses.
3.2. Analysis
The final dataset extracted from the survey contained (i) song ratings from each user of at least
10 songs, (ii) the participant’s personality traits (Agreeableness, Openness to (new) experience,
Conscientiousness, Extraversion and Neuroticism), and (iii) levels of inclination towards un-
substantiated beliefs (ASGS, Religious Belief, Psi, Superstition, Spiritualism, Precognition and
Conspiracy Beliefs). We augmented this dataset by adding our 10 song categories (themes,
discussed in Section 3.1), these were: Pain, Rebellion, Desire, Inspiration, Aspiration, Nostalgia,
Breakup, Escapism, and Confusion. Each song was mapped to one category. Once mapped, the
ratings of the songs in each category were aggregated and averaged for each user. This gave us
an average rating for each category for each user.
To address RQ1 we performed correlational analysis on the derived data. Specifically, we
analyzed the correlations between (i) the average rating of the participant per song category, (ii)
the participant’s personality scores and (iii) the participant’s scores on our scales for unverified
beliefs (ASGS, BCTI, and the 5 RPBS scores). We used the Pearson Correlation coefficient to
derive correlations. The results are presented in Section 4.
To address RQ2, we employed three models (i) Linear Regression, (ii) Decision Trees and
(iii) Matrix Factorization using Singular Value Decomposition (SVD) to predict beliefs based on
music preferences and compared them to a Mean Baseline. For the purpose of this paper, we
present our evaluation results for Singular Value Decomposition (SVD) our best-performing
model2 . A six-fold cross-validation was performed, and model performance was evaluated based
on the Root Mean Squared Error (RMSE) of the predicted scores for each category of unverified
beliefs (ASGS, Religious Belief, Psi, Superstition, Spiritualism, Precognition and Conspiracy
Beliefs). The results are presented in Section 4.
4. Results
4.1. Correlational Analysis
We now present our correlational analysis between (i) preferences in music categories, (ii) Big 5
Personality traits and (iii) unverified belief constructs. The correlational coefficient presented
here is the Pearson Coefficient which ranges between -1 and 1, where the closer a coefficient is
to 1 the stronger the positive correlation is, while the opposite is true for coefficients leaning
towards -1.
Figure 1a shows the correlation between (i) Big 5 Personality traits and (ii) preferences in
music categories. The user’s music preferences are operationalized as the average rating that
the participant provided in each category of songs. We can see that most of these correlations
are weak, even though previous work has shown that musical preferences and personality traits
are related [1]. This might indicate an issue with the characteristics and/or size of the sample.
Figure 1b shows the correlation between (i) Big 5 Personality traits and (ii) unverified belief
constructs. We can see some weak correlations between these constructs. For example, there
are weak correlations between (i) Conspiracy belief and Neuroticism, (ii) Conspiracy belief and
Conscientiousness and (iii) Superstition and Conscientiousness.
Figure 1c plots the correlations between (i) the average rating that the participant provided
in each category of songs (delineated in Section 3) and (ii) the participant’s unverified beliefs.
Music categories are delineated in Section 3. The unverified belief constructs are: Consipracy
theories (gcb), Australian Sheep-Goat (asgs), Religious Belief (trb), Psi (psi), Superstition (ss),
Spiritualism (sp), Precognition (pc).
From Figure 1c we can see that there are some weak correlations. For example, (i) preference
for the ”pain” song category is positively correlated with the Australian Sheep-Goat Scale
(asgs) and (ii) preference for songs in the ”escapism” category is negatively correlated with
superstitious beliefs (ss).
The majority of the correlations in Figure 1 are weak, even when a strong correlation was
expected, such as between Big 5 Personality traits and music preferences (as shown in [1]). The
2
These results are sufficient to answer the research question.
(a) (b)
(c)
Figure 1: Correlationas between (a) the user’s Beliefs (x-axis) and Personality Traits (y-axis), (b) the
user’s Personality Traits (x-axis) and the Average Rating Per Music Theme (y-axis) and (c) Beliefs (x-
axis) and the Average Rating Per Music Theme (y-axis). Music categories and personality traits are
delineated in Section 3. The unverified beliefs are: Consipracy theories (gcb), Australian Sheep-Goat
(asgs), Religious Belief (trb), Psi (psi), Superstition (ss), Spiritualism (sp), Precognition (PC).
results from our correlational analysis are inconclusive and point to the need of a more detailed
analysis on a larger sample of participants.
4.2. Prediction
The performance of our models for predicting unverified beliefs based on music preferences
was evaluated using Root Mean Squared Error (RMSE). Table 1 summarizes the average RMSE
values test sets across all folds and for all of the belief constructs. Models compared are, for
each belief construct, (i) our best-performing model i.e. Singular Value Decomposition (SVD)
and (ii) our mean baseline. The RMSE results indicate that the baseline model was consistent in
Table 1
Average RMSE results per unverified belief construct for SVD and baseline models. The unverified belief
construct are: Consipracy theories (GCB), Australian Sheep-Goat (ASGS), Religious Belief (TRB), Psi
(PSI), Superstition (SS), Spiritualism (SP), Precognition (PC). All predicted belief construct quantifiers
(GCB, TRB, PSI, SS and PC) are in the range of 1 to 7, except for the Australian Sheep-Goat (ASGS),
which is in the range of 0 to 36.
Belief Construct SVD RMSE Mean Baseline RMSE
PC 1.368 1.345
SP 1.341 1.306
SS 1.192 1.144
PSI 1.309 1.288
TRB 1.293 1.247
ASGS 1.277 1.247
GCB 1.003 0.978
beating the SVD model in achieving lower RMSE values across folds. This suggests that the
baseline model is more effective that the SVD model in capturing underlying patterns in the
data. While the SVD model also performs reasonably well, the baseline model proves to be the
better choice for this specific task. The baseline model also has a narrower error distribution,
which implies that the ratings are closer to their actual values, whereas the wider distribution
of the errors, which can be addressed with more advanced models.
5. Discussion and Conclusion
What the findings in this study highlighted is the importance of selecting the correct model in
predictive analysis. The dominance of the baseline model over the SVD model, underlines the
need of a detailed evaluation of different modeling approaches when dealing with complex data.
This insight can guide future research.
Another significant point is the enhancement of the SVD model in future research. For one,
gathering a more diverse and extensive dataset can greatly improve the performance of the
SVD model. Additionally, some fine-tuning the hyperparameters of the SVD model, might also
enhance its performance. Given more time, conducting more extensive experiments could lead
to different results, as well.
Several limitations of this study can be addressed in future work: (i) Data Limitation: Lack
of sufficient and diverse data is a limit for the models performance. Future studies should aim
to collect more comperhensive datasets; (ii) More Exploration: A limited number of models
have been explored in this work. Future research should consider introducing a more broader
spectrum of models and algorithms to find the most effective approach; (iii) Computational Re-
sources: Access to more powerful computing resources could potentially enable the exploration
of more complex models.
In conclusion, this study investigates the potential for predicting unverified beliefs based on
music preferences. While the results indicate some associations, the findings did not conclu-
sively demonstrate a predictive relationship. Several limitations, including insufficient sample
size and a limited exploration of predictive models, likely impacted the study’s outcomes.
These limitations underscore the need for more comprehensive data and the application of
more sophisticated models in future research. Despite these challenges, this work serves as a
foundation for further investigation, offering valuable insights that can guide future efforts to
better understand and enhance the predictive relationship between digital behaviour and belief
systems.
References
[1] Ferwerda, Bruce, Tkalcic, Marko, Schedl, Markus, Personality traits and music genre
preferences: How music taste varies over age groups 1922 (2017).
[2] E. Motamedi, D. K. Kholgh, S. Saghari, M. Elahi, F. Barile, M. Tkalcic, Predicting movies’
eudaimonic and hedonic scores: A machine learning approach using metadata, audio
and visual features, Information Processing & Management 61 (2024) 103610. URL:
https://linkinghub.elsevier.com/retrieve/pii/S0306457323003473. doi:doi: 10.1016/j.ipm.
2023.103610.
[3] V. Preniqi, K. Kalimeri, C. Saitis, Soundscapes of morality: Linking music preferences and
moral values through lyrics and audio, PloS One 18 (2023) e0294402. doi:doi: 10.1371/
journal.pone.0294402.
[4] I. Andersson, J. Persson, P. Kajonius, Even the stars think that i am superior: Personality,
intelligence and belief in astrology, Personality and Individual Differences 187 (2022)
111389. URL: https://linkinghub.elsevier.com/retrieve/pii/S0191886921007686. doi:doi: 10.
1016/j.paid.2021.111389.
[5] K. Drinkwater, A. Denovan, N. Dagnall, A. Parker, The australian sheep-goat scale: An
evaluation of factor structure and convergent validity, Frontiers in Psychology 9 (2018)
1594. doi:doi: 10.3389/fpsyg.2018.01594.
[6] M. A. Thalbourne, P. S. Delin, A new instrument for measuring the sheep-goat variable: Its
psychometric properties and factor structure., Journal of the society for psychical research
59 (1993) 172–186.
[7] J. J. Tobacyk, Revised paranormal belief scale, 2013. URL: https://doi.apa.org/doi/10.1037/
t14015-000. doi:doi: 10.1037/t14015-000, institution: American Psychological Association.
[8] A. Lantian, D. Muller, C. Nurra, K. M. Douglas, Measuring belief in conspiracy theo-
ries: Validation of a french and english single-item scale, International Review of Social
Psychology 29 (2016) 1. URL: https://www.rips-irsp.com/article/10.5334/irsp.8/. doi:doi:
10.5334/irsp.8.
[9] B. Rammstedt, O. P. John, Measuring personality in one minute or less: A 10-item short
version of the big five inventory in english and german, Journal of Research in Personality
41 (2007) 203–212. URL: https://linkinghub.elsevier.com/retrieve/pii/S0092656606000195.
doi:doi: 10.1016/j.jrp.2006.02.001.
[10] D. H. Henard, C. L. Rossetti, All you need is love?: Communication insights from pop
music’s number-one hits, Journal of Advertising Research 54 (2014) 178–191. URL: http:
//www.journalofadvertisingresearch.com/lookup/doi/10.2501/JAR-54-2-178-191. doi:doi:
10.2501/JAR-54-2-178-191.
[11] R. R. McCrae, O. P. John, An introduction to the five-factor model and its applications.,
Journal of personality 60 2 (1992) 175–215.