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.
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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?
https://markotkalcic.com/ (M. Tkalčič) CEUR Workshop Proceedings
Previous research has established a strong link between personality traits and preferences.
Ferwerda et al. [
In the context of unverified beliefs, Drinkwater et al. [
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
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 1These 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 ifve survey versions for maximum ratings. Each of these five survey versions was administered to a diferent 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) [
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.
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 unsubstantiated 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 coeficient 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.
We now present our correlational analysis between (i) preferences in music categories, (ii) Big 5 Personality traits and (iii) unverified belief constructs. The correlational coeficient presented here is the Pearson Coeficient which ranges between -1 and 1, where the closer a coeficient is to 1 the stronger the positive correlation is, while the opposite is true for coeficients 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 [
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 [
(a)
(b) (c) results from our correlational analysis are inconclusive and point to the need of a more detailed analysis on a larger sample of participants.
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 beating the SVD model in achieving lower RMSE values across folds. This suggests that the baseline model is more efective 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.
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 diferent 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 diferent results, as well.
Several limitations of this study can be addressed in future work: (i) Data Limitation: Lack of suficient 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 efective approach; (iii) Computational Resources: 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 conclusively demonstrate a predictive relationship. Several limitations, including insuficient 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, ofering valuable insights that can guide future eforts to better understand and enhance the predictive relationship between digital behaviour and belief systems.