Temporal Success Analyses in Music Collaboration Networks: Brazilian and Global Scenarios

Autores

DOI:

https://doi.org/10.33871/23179937.2023.11.2.7185

Palavras-chave:

Sucesso musical, Perfis de Colaboração, Análise de Redes Sociais

Resumo

Collaboration is a part of the music industry and has increased over recent decades; but little do we know about its effects on success and evolution. Our goal is to analyze how success has evolved over collaboration networks and compare its global scenario to a local, thriving one: the Brazilian music industry. Specifically, we build collaboration networks from data collected from Spotify's Global and Brazilian daily charts, analyze them and identify collaboration profiles in such networks. Analyses over their topological characteristics reveal collaboration patterns mapped into four different profiles: Standard, Niche, Ephemeral and Absent, where the two first have a higher level of success. Furthermore, we do deeper by evaluating the temporal evolution of such profiles through case studies: pop and k-pop globally, and pop and forró in Brazil. Overall, our findings emphasize the importance of collaboration profiles in assessing success, and show differences between the global and Brazilian scenarios.

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Biografia do Autor

Mariana O. Silva, Universidade Federal de Minas Gerais

Mariana O. Silva is a Ph.D. student in the Computer Science Graduate Program at Universidade Federal de Minas Gerais (UFMG). She received the B.Sc. and M.Sc. degrees at UFMG in 2017 and 2020, respectively. She was part of the Apoena Project as an undergraduate research assistant and the Bàde Project as a M.Sc. student. She is currently a member of the Laboratory of Interdisciplinary Computer Science (CS+X) and Data Scientist in the Analytical Capabilities Project. Her research interests include Data Science, Machine Learning and Social Network Analysis.

Gabriel P. Oliveira, Universidade Federal de Minas Gerais

Gabriel P. Oliveira is a Ph.D. student in the Computer Science Graduate Program at Universidade Federal de Minas Gerais (UFMG). He received the B.Sc. and M.Sc. degrees in Computer Science from UFMG in 2018 and 2021, respectively. He is currently a member of the Laboratory of Interdisciplinary Computer Science (CS+X) and Data Scientist in the Analytical Capabilities Project. His research interests include Social Computing, Data Science and Social Network Analysis, with a strong emphasis in collaborative domains.

Danilo B. Seufitelli, Universidade Federal de Minas Gerais

Danilo B. Seufitelli is a Ph.D. student in Computer Science at Universidade Federal de Minas Gerais (UFMG). He received the M.Sc. in Computer Science at the same university in December 2016. He received his Bachelor degree in Information Systems from Universidade Federal do Espírito Santo (UFES) in 2014. He is a member of the Laboratory of Interdisciplinary Computer Science (CS+X), and his research focuses on data analysis on collaborative domains, such as music, social networks, and open government data. Recently, he has been working on Bàde Project (Metrics on Data from/to Social Networks in Different Contexts).

Mirella M. Moro, UFMG

Mirella M. Moro is an associate professor at the Computer Science department at UFMG (Belo Horizonte, Brazil). She holds a Ph.D. in Computer Science (University of California Riverside - UCR, 2007), and MSc and BSc in Computer Science as well (UFRGS, Brazil). She was a member of the ACM Education Council (2009-2018) and the Education Director of SBC (Brazilian Computer Society, 2009-2015), where she’s currently a Council Member and part of the SBC Meninas Digitais (Digital Girls) Steering Committee. Her research interests include Data-driven Research, Social Analysis, Gender Diversity, and Computer Science Education. She is also an advocate for increasing women participation in Computing, coordinating projects such as BitGirls and Bytes & Elas.

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Publicado

03.08.2023

Como Citar

O. Silva, M., P. Oliveira, G., B. Seufitelli, D., & M. Moro, M. (2023). Temporal Success Analyses in Music Collaboration Networks: Brazilian and Global Scenarios. Revista Vórtex, 11(2), 1–27. https://doi.org/10.33871/23179937.2023.11.2.7185

Edição

v. 11 n. 2 (2023): Revista Vórtex

Seção

Artigos

Licença

Copyright (c) 2023 Mirella M. Moro, Mariana O. Silva, Gabriel P. Oliveira, Danilo B. Seufitelli

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Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.

Autores mantêm os direitos autorais e concedem à revista o direito de primeira publicação, com o trabalho simultaneamente licenciado sob a Licença Creative Commons Attribution que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista.