Do Music Preferences Reflect Cultural Values? A Cross-National Analysis Using Music Embedding and World Values Survey

2025-06-16

AI Summary

This study investigates whether national-level music preferences reflect deep cultural values. We construct multimodal music representations from YouTube music charts across 62 countries, integrating CLAP audio embeddings, LP-MusicCaps semantic annotations, and GPT-generated descriptive summaries. Using t-SNE visualization and contrastive learning–based clustering, we systematically align musical patterns with the World Values Survey (WVS) cultural clusters. Our work achieves the first cross-modal, statistically significant alignment between music preferences and global cultural value dimensions (MANOVA and chi-square tests, *p* < 0.001). Residual analysis reveals consistent overrepresentation of East Asian, Islamic, and English-speaking cultural regions in music-based clustering. Results demonstrate that popular music preferences robustly encode cultural boundaries and serve as a valid, large-scale proxy for macro-level cultural differentiation.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

This study explores the extent to which national music preferences reflect underlying cultural values. We collected long-term popular music data from YouTube Music Charts across 62 countries, encompassing both Western and non-Western regions, and extracted audio embeddings using the CLAP model. To complement these quantitative representations, we generated semantic captions for each track using LP-MusicCaps and GPT-based summarization. Countries were clustered based on contrastive embeddings that highlight deviations from global musical norms. The resulting clusters were projected into a two-dimensional space via t-SNE for visualization and evaluated against cultural zones defined by the World Values Survey (WVS). Statistical analyses, including MANOVA and chi-squared tests, confirmed that music-based clusters exhibit significant alignment with established cultural groupings. Furthermore, residual analysis revealed consistent patterns of overrepresentation, suggesting non-random associations between specific clusters and cultural zones. These findings indicate that national-level music preferences encode meaningful cultural signals and can serve as a proxy for understanding global cultural boundaries.

Problem

• Investigating if music preferences reflect cultural values
• Analyzing cross-national music data for cultural alignment
• Validating music as a proxy for cultural boundaries

Innovation

• Used CLAP model for music embedding extraction
• Generated semantic captions with LP-MusicCaps and GPT
• Applied t-SNE for clustering visualization