# On the Interplay between Musical Preferences and Personality through the Lens of Language

arXiv: https://arxiv.org/abs/2508.18208 · HTML: https://mlsanity.com/paper/2508.18208/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 25 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [Large Language Models Can Infer Psychological Dispositions of Social Media Users](https://mlsanity.com/paper/2309.08631/) (2023)
- [Psycholinguistic Tripartite Graph Network for Personality Detection](https://mlsanity.com/paper/2106.04963/) (2021)
- [Native Language Cognate Effects on Second Language Lexical Choice](https://mlsanity.com/paper/1805.09590/) (2018)
- [Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis](https://mlsanity.com/paper/2104.11612/) (2021)
- [Quantifying Cognitive Factors in Lexical Decline](https://mlsanity.com/paper/2110.05775/) (2021)
- [Exploration of Gender Differences in COVID-19 Discourse on Reddit](https://mlsanity.com/paper/2008.05713/) (2020)

## Built on by (most cited first, 1 of 1)

- [When Words Divide: Diachronic Ideological Polarization in Political Discourse on Social Media](https://mlsanity.com/paper/2608.01176/) (2026)

## Abstract (arXiv)

Music serves as a powerful reflection of individual identity, often aligning with deeper psychological traits. Prior research has established correlations between musical preferences and personality, while separate studies have demonstrated that personality is detectable through linguistic analysis. Our study bridges these two research domains by investigating whether individuals' musical preferences leave traces in their spontaneous language through the lens of the Big Five personality traits (Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism). Using a carefully curated dataset of over 500,000 text samples from nearly 5,000 authors with reliably identified musical preferences, we build advanced models to assess personality characteristics. Our results reveal significant personality differences across fans of five musical genres. We release resources for future research at the intersection of computational linguistics, music psychology and personality analysis.