"More Than Words": Linking Music Preferences and Moral Values Through Lyrics

TL;DR

This research demonstrates that analyzing song lyrics can predict individuals' moral values, revealing a significant link between music preferences and morality through machine learning models.

Contribution

It introduces a novel approach using lyrical features and machine learning to infer moral values from music preferences, highlighting lyrics' predictive power beyond demographics.

Findings

Lyrics from preferred artists predict moral virtues with moderate accuracy.

Hierarchy and tradition are more predictable than empathy and equality.

Music preferences provide unique insights into individual moral values.

Abstract

This study explores the association between music preferences and moral values by applying text analysis techniques to lyrics. Harvesting data from a Facebook-hosted application, we align psychometric scores of 1,386 users to lyrics from the top 5 songs of their preferred music artists as emerged from Facebook Page Likes. We extract a set of lyrical features related to each song's overarching narrative, moral valence, sentiment, and emotion. A machine learning framework was designed to exploit regression approaches and evaluate the predictive power of lyrical features for inferring moral values. Results suggest that lyrics from top songs of artists people like inform their morality. Virtues of hierarchy and tradition achieve higher prediction scores ($.20 \leq r \leq .30$) than values of empathy and equality ($.08 \leq r \leq .11$), while basic demographic variables only account for a small part in the models' explainability. This shows the importance of music listening behaviours, as assessed via lyrical preferences, alone in capturing moral values. We discuss the technological and musicological implications and possible future improvements.