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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 persons 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).
Personal electronic devices including smartphones give access to behavioural signals that can be used to learn about the characteristics and preferences of individuals. In this study, we explore the connection between demographic and psychological at
One-shot anonymous unselfishness in economic games is commonly explained by social preferences, which assume that people care about the monetary payoffs of others. However, during the last ten years, research has shown that different types of unselfi
Autonomous Vehicles (AVs) raise important social and ethical concerns, especially about accountability, dignity, and justice. We focus on the specific concerns arising from how AV technology will affect the lives and livelihoods of professional and s
Online streaming services have become the most popular way of listening to music. The majority of these services are endowed with recommendation mechanisms that help users to discover songs and artists that may interest them from the vast amount of m
Many researchers have studied student academic performance in supervised and unsupervised learning using numerous data mining techniques. Neural networks often need a greater collection of observations to achieve enough predictive ability. Due to the