Correlating Big Five Primary Personality Dimensions with Musical Preferences

  • Luiz Delando Santos Moreira Júnior Universidade Federal de Pernambuco
  • Guilherme Ribeiro Eulálio Cabral Universidade Federal do Vale do São Francisco
  • Giordano Ribeiro Eulálio Cabral Universidade Federal do Vale do São Francisco


According to J Clement in 2019 an estimated 2.95 billion people were using social media worldwide, with a projection of 3.43 billion in 2023. Social networks increasingly play a fundamental role in the way of expressing our-selves and relating to others, in which it is directly linked to our personality. Several studies relate a persons personality to the most diverse areas, such as predicting political orientation, classifying gender, or even measuring the level of success at work. Wu Youyou, 2015 proposes an algorithm that can allegedly estimate the personality of a Facebook user better than his partner, based on his digital footprint, thanks to AI techniques and the massive number of inter-actions that users perform on their social media. The focus of this work is to relate the Big Five personality theory with musical preferences. Since social media posts register moments and impressions of users lives, and since music streaming platforms provide material about musical taste second Peter J RentFrow et al, 203, we used tools to extract the big five primary personality dimensions from Facebook and audio features from the Spotify Web API, and looked for correlations be-tween musical preferences and personality. Results show some correlations and anticorrelations found, pointing to further research with a broader range of features.

Palavras-chave: Music Information Retrieval, Music, Society, and Technology


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MOREIRA JÚNIOR, Luiz Delando Santos; CABRAL, Guilherme Ribeiro Eulálio; CABRAL, Giordano Ribeiro Eulálio. Correlating Big Five Primary Personality Dimensions with Musical Preferences. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO MUSICAL (SBCM), 18. , 2021, Recife. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 103-108. DOI: