"More Than Words": Linking Music Preferences and Moral Values Through Lyrics
Vjosa Preniqi, Kyriaki Kalimeri, Charalampos Saitis

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 () than values of empathy and equality (), while basic demographic variables only account for a…
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Taxonomy
TopicsMusic and Audio Processing · Music History and Culture · Neuroscience and Music Perception
MethodsALIGN
