"More Than Words": Linking Music Preferences and Moral Values Through Lyrics
This research addresses the problem of understanding how cultural consumption relates to personal values for psychologists and social scientists, but it is incremental as it applies existing text analysis methods to a new dataset.
This study investigated the link between music preferences and moral values by analyzing lyrics from users' favorite songs, finding that lyrics can predict moral values with correlations ranging from 0.08 to 0.30, with hierarchy and tradition values showing higher predictability than empathy and equality.
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.