Characterization of the Brazilian musical landscape: a study of regional preferences based on the Spotify charts
Resumo
In the digital age, streaming services such as Spotify have changed the way people consume music, highlighting the enormous influence these platforms have on the market. In the highly competitive music industry, it is crucial for independent artists in particular to maintain their popularity. This is especially true in countries like Brazil, where geographical and cultural differences influence music consumption patterns. Understanding these patterns is essential for effective marketing and production strategies. Despite previous research on music consumption, genre preferences and user behavior, there is a lack of detailed studies on the geographical and cultural distribution of music preferences in Brazil. Our study fills this gap by examining musical genre preferences and acoustic features of tracks across Brazilian regions over two years. We collected Spotify chart data from 2022 and 2023, modeled bipartite genre-city networks, and used backbone extraction methods to highlight significant genre preferences. Temporal analysis revealed patterns and persistence of musical preferences across cities, while clustering techniques revealed regional and cultural differences in acoustic features. Our results show that genre preferences are stable across Brazilian regions, with important genres emphasized by backbone networks. Persistence analysis suggests minimal changes over time, except during major holidays. Furthermore, Brazilian city clusters exhibit distinct acoustic patterns regardless of music genres, with notable differences in features such as liveliness, speechiness, and valence. This research provides new insights into regional musical diversity in Brazil and paves the way for future studies on cultural and geographical influences on music preferences.
Palavras-chave:
Music Preferences, Music Genre Networks, Regional Analysis, Spotify Charts, Music Data Mining
Referências
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Mariana Lopes Barata and Pedro Simoes Coelho. 2021. Music streaming services: understanding the drivers of customer purchase and intention to recommend. Heliyon 7, 8 (2021).
Gabriel Barbosa, Bruna Melo, Gabriel Oliveira, Mariana Silva, Danilo Seufitelli, and Mirella Moro. 2021. Hot Streaks in the Brazilian Music Market: A Comparison Between Physical and Digital Eras. In Anais do XVIII Simpósio Brasileiro de Computação Musical (Recife). SBC, Porto Alegre, RS, Brasil, 152–159. DOI: 10.5753/sbcm.2021.19440
Dogan Basaran and Keti Ventura. 2022. Exploring Digital Marketing In Entertainment Industry: A Case Of A Digital Music Platform. Journal of Management Marketing and Logistics 9, 3 (2022), 115–126.
Pablo Bello and David Garcia. 2021. Cultural Divergence in popular music: the increasing diversity of music consumption on Spotify across countries. Humanities and Social Sciences Communications 8, 1 (2021), 1–8.
Pauwke Berkers. 2012. Gendered scrobbling: Listening behaviour of young adults on Last. fm. Interactions: Studies in Communication & Culture 2, 3 (2012), 279–296.
Jean-Samuel Beuscart, Samuel Coavoux, and Jean-Baptiste Garrocq. 2023. Listening to music videos on YouTube. Digital consumption practices and the enviromental impact of streaming. Journal of Consumer Culture (2023), 654–671.
Michele Coscia. 2021. Noise Corrected Sampling of Online Social Networks. ACM Transactions on Knowledge Discovery from Data (TKDD) 15, 2 (2021), 1–21.
Anne Danielsen and Yngvar Kjus. 2019. The mediated festival: Live music as trigger of streaming and social media engagement. Convergence (2019).
Robert Dorfman. 1979. A formula for the Gini coefficient. The review of economics and statistics (1979), 146–149.
Maura Edmond. 2014. Here we go again: Music videos after YouTube. Television & New Media 15, 4 (2014), 305–320.
Falina Enriquez. 2022. Pernambuco and Bahia’s Musical “War”: Contemporary Music, Intraregional Rivalry, and Branding in Northeastern Brazil. Luso-Brazilian Review 59, 1 (2022), 22–60.
Ghazal Fazelnia, Eric Simon, Ian Anderson, Benjamin Carterette, and Mounia Lalmas. 2022. Variational User Modeling with Slow and Fast Features. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (Virtual Event, AZ, USA) (WSDM ’22). Association for Computing Machinery, New York, NY, USA, 271–279. DOI: 10.1145/3488560.3498477
Andres Ferraro, Xavier Serra, and Christine Bauer. 2021. What is fair? Exploring the artists’ perspective on the fairness of music streaming platforms. In IFIP conference on human-computer interaction. Springer, 562–584.
Carlos Henrique Gomes Ferreira, Fabricio Murai, Ana P. C. Silva, Martino Trevisan, Luca Vassio, Idilio Drago, Marco Mellia, and Jussara M. Almeida. 2022. On network backbone extraction for modeling online collective behavior. PLOS ONE 17, 9 (09 2022), 1–36. DOI: 10.1371/journal.pone.0274218
Jonathon Grasse. 2021. Musical Spaces and Deep Regionalism in Minas Gerais, Brazil. In Musical Spaces. Jenny Stanford Publishing, 5–21.
Jiawei Han, Jian Pei, and Hanghang Tong. 2022. Data mining: concepts and techniques. Morgan kaufmann.
John A Hartigan and Manchek A Wong. 1979. Algorithm AS 136: A k-means clustering algorithm. Journal of the royal statistical society. series c (applied statistics) 28, 1 (1979), 100–108.
David Hesmondhalgh. 2022. Streaming’s effects on music culture: Old anxieties and new simplifications. Cultural Sociology 16, 1 (2022), 3–24.
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Ian T Jolliffe and Jorge Cadima. 2016. Principal component analysis: a review and recent developments. Philosophical transactions of the royal society A: Mathematical, Physical and Engineering Sciences 374, 2065 (2016), 20150202.
Maarit Kinnunen, Harri Homi, and Antti Honkanen. 2020. Social sustainability in adolescents’ music event attendance. Sustainability 12, 22 (2020), 9419.
Conrad Lee and Padraig Cunningham. 2012. The Geographic Flow of Music. (042012). DOI: 10.1109/ASONAM.2012.237
Conrad Lee and Padraig Cunningham. 2023. Number of music streaming subscribers worldwide from the 1st half of 2019 to 3rd quarter 2023. (2023). [link]
Elisabeth Lex, Dominik Kowald, and Markus Schedl. 2020. Modeling Popularity and Temporal Drift of Music Genre Preferences. Trans. Int. Soc. Music. Inf. Retr. 3, 1 (2020), 17–30.
Renan S Linhares, José M Rosa, Carlos HG Ferreira, Fabricio Murai, Gabriel Nobre, and Jussara Almeida. 2022. Uncovering coordinated communities on twitter during the 2020 us election. In 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 80–87.
Riccardo Marcaccioli and Giacomo Livan. 2019. A pólya urn approach to information filtering in complex networks. Nature communications (2019).
Maria Luiza Botelho Mondelli, Luiz M. R. Gadelha Jr., and Artur Ziviani. 2018. O Que os Países Escutam: Analisando a Rede de Gêneros Musicais ao Redor do Mundo. In Anais do VII Brazilian Workshop on Social Network Analysis and Mining (Natal). SBC, Porto Alegre, RS, Brasil. DOI: 10.5753/brasnam.2018.3586
Jeremy Wade Morris. 2020. Music platforms and the optimization of culture. Social Media+ Society 6, 3 (2020), 2056305120940690.
Shane Murphy. 2020. Music marketing in the digital music industries–An autoethnographic exploration of opportunities and challenges for independent musicians. International Journal of Music Business Research 9, 1 (2020), 7–40.
Houssam Nassif, Kemal Oral Cansizlar, Mitchell Goodman, and SVN Vish-wanathan. 2018. Diversifying music recommendations. arXiv preprint arXiv:1810.01482 (2018).
Zachary P Neal. 2022. backbone: An R package to extract network backbones. PloS one 17, 5 (2022), e0269137.
Robert Nisbet, John Elder, and Gary D Miner. 2009. Handbook of statistical analysis and data mining applications. Academic press.
Ramy A Rahimi and Kyung-Hye Park. 2020. A comparative study of internet architecture and applications of online music streaming services: The impact on the global music industry growth. In 2020 8th International Conference on Information and Communication Technology (ICoICT). IEEE, 1–6.
Jing Ren and Robert J. Kauffman. [n. d.]. Understanding music track popularity in a social network.(2017). In Proceedings of the 25th European Conference on Information Systems ECIS, Guimarães, Portugal, June. 5–10.
Peter J Rousseeuw. 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics 20 (1987), 53–65.
M Ángeles Serrano, Marián Boguná, and Alessandro Vespignani. 2009. Extracting the multiscale backbone of complex weighted networks. Proceedings of the national academy of sciences 106, 16 (2009), 6483–6488.
Yading Song, Simon Dixon, and Marcus Pearce. 2012. A survey of music recommendation systems and future perspectives. In 9th international symposium on computer music modeling and retrieval, Vol. 4. Citeseer, 395–410.
Vilde Schanke Sundet and Marika Lüders. 2023. “Young people are on YouTube”: industry notions on streaming and youth as a new media generation. Journal of Media Business Studies 20, 3 (2023), 223–240.
Fernando Terroso-Saenz, Jesús Soto, and Andres Muñoz. 2023. Music Mobility Patterns: How Songs Propagate Around the World Through Spotify. Pattern Recognition 143 (2023), 109807.
Pier Paolo Tricomi, Luca Pajola, Luca Pasa, and Mauro Conti. 2024. "All of Me": Mining Users’ Attributes from their Public Spotify Playlists. In Companion Proceedings of the ACM on Web Conference 2024. Association for Computing Machinery, New York, NY, USA.
Gerald Van Belle. 2011. Statistical rules of thumb. John Wiley & Sons.
Gabriel Vaz de Melo, Ana Machado, and Lucas Carvalho. 2020. Music consumption in Brazil: an analysis of streaming reproductions. PragMATIZES - Revista Latino-Americana de Estudos em Cultura 10 (09 2020), 141.
Samuel F. Way, Jean Garcia-Gathright, and Henriette Cramer. 2020. Local Trends in Global Music Streaming. Proceedings of the International AAAI Conference on Web and Social Media 14, 1 (May 2020), 705–714. DOI: 10.1609/icwsm.v14i1.7336
Jacob Wolbert. 2023. Multiple Brasilidades: Musician-Market Negotiations within the Brazilian Midstream. Journal of Popular Music Studies 35, 3 (2023), 102–127.
Carlos Araujo, Marco Cristo, and Rafael Giusti. 2019. Predicting Music Popularity on Streaming Platforms. In Anais do XVII Simpósio Brasileiro de Computação Musical (São João del-Rei). SBC, Porto Alegre, RS, Brasil, 141–148. DOI: 10.5753/sbcm.2019.10436
Mariana Lopes Barata and Pedro Simoes Coelho. 2021. Music streaming services: understanding the drivers of customer purchase and intention to recommend. Heliyon 7, 8 (2021).
Gabriel Barbosa, Bruna Melo, Gabriel Oliveira, Mariana Silva, Danilo Seufitelli, and Mirella Moro. 2021. Hot Streaks in the Brazilian Music Market: A Comparison Between Physical and Digital Eras. In Anais do XVIII Simpósio Brasileiro de Computação Musical (Recife). SBC, Porto Alegre, RS, Brasil, 152–159. DOI: 10.5753/sbcm.2021.19440
Dogan Basaran and Keti Ventura. 2022. Exploring Digital Marketing In Entertainment Industry: A Case Of A Digital Music Platform. Journal of Management Marketing and Logistics 9, 3 (2022), 115–126.
Pablo Bello and David Garcia. 2021. Cultural Divergence in popular music: the increasing diversity of music consumption on Spotify across countries. Humanities and Social Sciences Communications 8, 1 (2021), 1–8.
Pauwke Berkers. 2012. Gendered scrobbling: Listening behaviour of young adults on Last. fm. Interactions: Studies in Communication & Culture 2, 3 (2012), 279–296.
Jean-Samuel Beuscart, Samuel Coavoux, and Jean-Baptiste Garrocq. 2023. Listening to music videos on YouTube. Digital consumption practices and the enviromental impact of streaming. Journal of Consumer Culture (2023), 654–671.
Michele Coscia. 2021. Noise Corrected Sampling of Online Social Networks. ACM Transactions on Knowledge Discovery from Data (TKDD) 15, 2 (2021), 1–21.
Anne Danielsen and Yngvar Kjus. 2019. The mediated festival: Live music as trigger of streaming and social media engagement. Convergence (2019).
Robert Dorfman. 1979. A formula for the Gini coefficient. The review of economics and statistics (1979), 146–149.
Maura Edmond. 2014. Here we go again: Music videos after YouTube. Television & New Media 15, 4 (2014), 305–320.
Falina Enriquez. 2022. Pernambuco and Bahia’s Musical “War”: Contemporary Music, Intraregional Rivalry, and Branding in Northeastern Brazil. Luso-Brazilian Review 59, 1 (2022), 22–60.
Ghazal Fazelnia, Eric Simon, Ian Anderson, Benjamin Carterette, and Mounia Lalmas. 2022. Variational User Modeling with Slow and Fast Features. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (Virtual Event, AZ, USA) (WSDM ’22). Association for Computing Machinery, New York, NY, USA, 271–279. DOI: 10.1145/3488560.3498477
Andres Ferraro, Xavier Serra, and Christine Bauer. 2021. What is fair? Exploring the artists’ perspective on the fairness of music streaming platforms. In IFIP conference on human-computer interaction. Springer, 562–584.
Carlos Henrique Gomes Ferreira, Fabricio Murai, Ana P. C. Silva, Martino Trevisan, Luca Vassio, Idilio Drago, Marco Mellia, and Jussara M. Almeida. 2022. On network backbone extraction for modeling online collective behavior. PLOS ONE 17, 9 (09 2022), 1–36. DOI: 10.1371/journal.pone.0274218
Jonathon Grasse. 2021. Musical Spaces and Deep Regionalism in Minas Gerais, Brazil. In Musical Spaces. Jenny Stanford Publishing, 5–21.
Jiawei Han, Jian Pei, and Hanghang Tong. 2022. Data mining: concepts and techniques. Morgan kaufmann.
John A Hartigan and Manchek A Wong. 1979. Algorithm AS 136: A k-means clustering algorithm. Journal of the royal statistical society. series c (applied statistics) 28, 1 (1979), 100–108.
David Hesmondhalgh. 2022. Streaming’s effects on music culture: Old anxieties and new simplifications. Cultural Sociology 16, 1 (2022), 3–24.
David C Howell. 1992. Statistical methods for psychology. PWS-Kent Publishing Co.
Julie Jiang, Aditiya Ponnada, Ang Li, Ben Lacker, and Samuel F Way. 2024. A Genre-Based Analysis of New Music Streaming at Scale. In Proceedings of the 16th ACM Web Science Conference (WEBSCI ’24). Association for Computing Machinery, New York, NY, USA, 191–201. DOI: 10.1145/3614419.3644002
Ian T Jolliffe and Jorge Cadima. 2016. Principal component analysis: a review and recent developments. Philosophical transactions of the royal society A: Mathematical, Physical and Engineering Sciences 374, 2065 (2016), 20150202.
Maarit Kinnunen, Harri Homi, and Antti Honkanen. 2020. Social sustainability in adolescents’ music event attendance. Sustainability 12, 22 (2020), 9419.
Conrad Lee and Padraig Cunningham. 2012. The Geographic Flow of Music. (042012). DOI: 10.1109/ASONAM.2012.237
Conrad Lee and Padraig Cunningham. 2023. Number of music streaming subscribers worldwide from the 1st half of 2019 to 3rd quarter 2023. (2023). [link]
Elisabeth Lex, Dominik Kowald, and Markus Schedl. 2020. Modeling Popularity and Temporal Drift of Music Genre Preferences. Trans. Int. Soc. Music. Inf. Retr. 3, 1 (2020), 17–30.
Renan S Linhares, José M Rosa, Carlos HG Ferreira, Fabricio Murai, Gabriel Nobre, and Jussara Almeida. 2022. Uncovering coordinated communities on twitter during the 2020 us election. In 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 80–87.
Riccardo Marcaccioli and Giacomo Livan. 2019. A pólya urn approach to information filtering in complex networks. Nature communications (2019).
Maria Luiza Botelho Mondelli, Luiz M. R. Gadelha Jr., and Artur Ziviani. 2018. O Que os Países Escutam: Analisando a Rede de Gêneros Musicais ao Redor do Mundo. In Anais do VII Brazilian Workshop on Social Network Analysis and Mining (Natal). SBC, Porto Alegre, RS, Brasil. DOI: 10.5753/brasnam.2018.3586
Jeremy Wade Morris. 2020. Music platforms and the optimization of culture. Social Media+ Society 6, 3 (2020), 2056305120940690.
Shane Murphy. 2020. Music marketing in the digital music industries–An autoethnographic exploration of opportunities and challenges for independent musicians. International Journal of Music Business Research 9, 1 (2020), 7–40.
Houssam Nassif, Kemal Oral Cansizlar, Mitchell Goodman, and SVN Vish-wanathan. 2018. Diversifying music recommendations. arXiv preprint arXiv:1810.01482 (2018).
Zachary P Neal. 2022. backbone: An R package to extract network backbones. PloS one 17, 5 (2022), e0269137.
Robert Nisbet, John Elder, and Gary D Miner. 2009. Handbook of statistical analysis and data mining applications. Academic press.
Ramy A Rahimi and Kyung-Hye Park. 2020. A comparative study of internet architecture and applications of online music streaming services: The impact on the global music industry growth. In 2020 8th International Conference on Information and Communication Technology (ICoICT). IEEE, 1–6.
Jing Ren and Robert J. Kauffman. [n. d.]. Understanding music track popularity in a social network.(2017). In Proceedings of the 25th European Conference on Information Systems ECIS, Guimarães, Portugal, June. 5–10.
Peter J Rousseeuw. 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics 20 (1987), 53–65.
M Ángeles Serrano, Marián Boguná, and Alessandro Vespignani. 2009. Extracting the multiscale backbone of complex weighted networks. Proceedings of the national academy of sciences 106, 16 (2009), 6483–6488.
Yading Song, Simon Dixon, and Marcus Pearce. 2012. A survey of music recommendation systems and future perspectives. In 9th international symposium on computer music modeling and retrieval, Vol. 4. Citeseer, 395–410.
Vilde Schanke Sundet and Marika Lüders. 2023. “Young people are on YouTube”: industry notions on streaming and youth as a new media generation. Journal of Media Business Studies 20, 3 (2023), 223–240.
Fernando Terroso-Saenz, Jesús Soto, and Andres Muñoz. 2023. Music Mobility Patterns: How Songs Propagate Around the World Through Spotify. Pattern Recognition 143 (2023), 109807.
Pier Paolo Tricomi, Luca Pajola, Luca Pasa, and Mauro Conti. 2024. "All of Me": Mining Users’ Attributes from their Public Spotify Playlists. In Companion Proceedings of the ACM on Web Conference 2024. Association for Computing Machinery, New York, NY, USA.
Gerald Van Belle. 2011. Statistical rules of thumb. John Wiley & Sons.
Gabriel Vaz de Melo, Ana Machado, and Lucas Carvalho. 2020. Music consumption in Brazil: an analysis of streaming reproductions. PragMATIZES - Revista Latino-Americana de Estudos em Cultura 10 (09 2020), 141.
Samuel F. Way, Jean Garcia-Gathright, and Henriette Cramer. 2020. Local Trends in Global Music Streaming. Proceedings of the International AAAI Conference on Web and Social Media 14, 1 (May 2020), 705–714. DOI: 10.1609/icwsm.v14i1.7336
Jacob Wolbert. 2023. Multiple Brasilidades: Musician-Market Negotiations within the Brazilian Midstream. Journal of Popular Music Studies 35, 3 (2023), 102–127.
Publicado
14/10/2024
Como Citar
MOURA, Filipe A. S.; FERREIRA, Carlos H. G.; LIMA, Helen C. S. C..
Characterization of the Brazilian musical landscape: a study of regional preferences based on the Spotify charts. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 30. , 2024, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 80-88.
DOI: https://doi.org/10.5753/webmedia.2024.242290.
