Modelling Moral Traits with Music Listening Preferences and Demographics

Preniqi, Vjosa; Kalimeri, Kyriaki; Saitis, Charalampos

doi:10.1007/978-3-031-35382-6_14

Cited by 1 publication

(5 citation statements)

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“…Building on Tagg’s socio-semantic view of musical meaning [ 69 ], we hypothesised that audio features play an important role in comprehending how people align their music preferences with moral worldviews. Despite difficulties in predicting Care and Fairness foundations reported previously [ 18 , 66 ], we improved the predicting results for these foundations mainly when adding acoustic features. This was in line with correlation results which suggested that individuals with higher scores for Care and Fairness preferred artists whose songs are more acoustic and include fewer speech elements.…”

Section: Discussionmentioning

confidence: 90%

“…According to the moral prediction results in recent studies, foundations related to Care and Fairness were challenging to predict [ 18 , 66 ]. Our results again showed that moral values related to Care and Fairness are more complex to predict than values related to Authority, Purity, and Loyalty.…”

Section: Resultsmentioning

confidence: 99%

“…Those concerned more about ingroup and Binding values liked artists whose songs contained more romantic topics and disliked lyrics that implied negative emotions such as sadness, fear, or disgust [ 18 ]. However, as in the previous studies [ 55 , 66 ], the Individualising foundations remained challenging to predict. Here, we extend this work by adding a range of low- and high-level audio features separately and in a multimodal framework, together with the lyrics, to improve the present results further, building on Tagg’s socio-semantic view of musical meaning [ 69 ] and motivated by recent findings linking individual audio features of preferred music to personality traits and values [ 4 , 7 , 16 ].…”

Section: Introductionmentioning

confidence: 85%

“…In terms of morality and music studies, an association between moral values, demographics, and musical genres was explored by Preniqi et al [ 66 ] using ad-hoc survey data. This study found that people with higher levels of Binding foundations tend to like country and Christian music.…”

Section: Introductionmentioning

confidence: 99%

“…[ 55 ]). Furthermore, the authors explained that adding demographic information (e.g., age, gender, political views, education) improved MFT predictions only to a limited degree, showing the ability of music preferences alone to describe one’s moral values [ 66 ].…”

Section: Introductionmentioning

confidence: 99%

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Soundscapes of morality: Linking music preferences and moral values through lyrics and audio

Preniqi,

Kalimeri,

Saitis

2023

PLoS ONE

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Music is a fundamental element in every culture, serving as a universal means of expressing our emotions, feelings, and beliefs. This work investigates the link between our moral values and musical choices through lyrics and audio analyses. We align the psychometric scores of 1,480 participants to acoustics and lyrics features obtained from the top 5 songs of their preferred music artists from Facebook Page Likes. We employ a variety of lyric text processing techniques, including lexicon-based approaches and BERT-based embeddings, to identify each song’s narrative, moral valence, attitude, and emotions. In addition, we extract both low- and high-level audio features to comprehend the encoded information in participants’ musical choices and improve the moral inferences. We propose a Machine Learning approach and assess the predictive power of lyrical and acoustic features separately and in a multimodal framework for predicting moral values. Results indicate that lyrics and audio features from the artists people like inform us about their morality. Though the most predictive features vary per moral value, the models that utilised a combination of lyrics and audio characteristics were the most successful in predicting moral values, outperforming the models that only used basic features such as user demographics, the popularity of the artists, and the number of likes per user. Audio features boosted the accuracy in the prediction of empathy and equality compared to textual features, while the opposite happened for hierarchy and tradition, where higher prediction scores were driven by lyrical features. This demonstrates the importance of both lyrics and audio features in capturing moral values. The insights gained from our study have a broad range of potential uses, including customising the music experience to meet individual needs, music rehabilitation, or even effective communication campaign crafting.

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Section: Discussionmentioning

confidence: 90%

Section: Resultsmentioning

confidence: 99%