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

Visualizações: 101

Autores/as

DOI:

https://doi.org/10.33871/23179937.2023.11.2.7185

Palabras clave:

Musical success, Collaboration profiles, Social Network Analysis

Resumen

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.

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

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, Universidade Federal de Minas Gerais

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.

Citas

ABEL, Fabian; DIAZ-AVILES, Ernesto; HENZE, Nicola; KRAUSE, Daniel; SIEHNDEL, Patrick. Analyzing the Blogosphere for Predicting the Success of Music and Movie Products. In: Proceedings of International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Odense, Denmark, 2010. Doi: 10.1109/ASONAM.2010.50 pp. 276–280. DOI: https://doi.org/10.1109/ASONAM.2010.50

ARAUJO, Carlos S.; NETO, Rayol M.; NAKAMURA, Fabiola G.; NAKAMURA, Eduardo F.. Predicting Music Success Based on Users’ Comments on Online Social Networks. In: Proceedings of Brazilian Symposium on Multimedia and the Web (WebMedia). Gramado, Brazil, 2017. Doi: 10.1145/3126858.3126885 pp. 149–156. DOI: https://doi.org/10.1145/3126858.3126885

BEZDEK, James C. Pattern Recognition with Fuzzy Objective Function Algorithms. Springer, 1981. ISBN 978-1-4757-0452-5 DOI: https://doi.org/10.1007/978-1-4757-0450-1_1

CALEFATO, Fabio; IAFFALDANO, Giuseppe; LANUBILE, Filippo. Collaboration Success Factors in an Online Music Community. In: ACM GROUP. Sanibel Island, USA, 2018. Doi: 10.1145/3148330.3148346 pp. 61–70. DOI: https://doi.org/10.1145/3148330.3148346

CATTEL, Raymond B. The scree test for the number of factors. Multivariate Behavioral Research. Vol. 1, no. 2, pp. 245–276, 1966. DOI: https://doi.org/10.1207/s15327906mbr0102_10

COSIMATO, Alberto; PRISCO, Roberto; GUARINO, Afonso; MALANDRINO, Delfina; LETTIERI, Nicola; SORRENTINO, Giuseppe; ZACCAGNINO, Rocco. The Conundrum of Success in Music: Playing it or Talking About it? IEEE Access. Vol. 7, pp. 123 289–123 298, 2019. Doi: 10.1109/ACCESS.2019.2937743 DOI: https://doi.org/10.1109/ACCESS.2019.2937743

COSTELLO, Anna B.; OSBORNE, Jason. Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical assessment, research, and evaluation. Vol. 10, no. 1, p. 7, 2005.

GIENAPP, Lukas; KRUCKENBERG, Clara; BURGHARDT, Manuel. Topological properties of music collaboration networks: The case of Jazz and Hip Hop. Digital Humanities Quarterly. Vol. 15, no. 1, 2021. [Online]. Available: http://www.digitalhumanities.org/dhq/vol/15/1/000504/000504.html

GRANOVETTER, Mark S. The Strength of Weak Ties. American Journal of Sociology. Vol. 78, n. 6, pp. 1360-1380, 1973. DOI: https://doi.org/10.1086/225469

HUMPREYS, Lloyd G.; MONTANELLI JR, Richard G. An investigation of the parallel analysis criterion for determining the number of common factors. Multivariate Behavioral Research. Vol. 10, no. 2, pp. 193–205, 1975. DOI: https://doi.org/10.1207/s15327906mbr1002_5

KIM, Seon Tae; OH, Joo Hee. Music intelligence: Granular data and prediction of top ten hit songs. Decision Support Systems. Vol. 145, p. 113535, 2021. Doi: 10.1016/j.dss.2021.113535 DOI: https://doi.org/10.1016/j.dss.2021.113535

LIMA, Raul A.; SOUZA, Rômulo C. C.; LOPES, Hélio; BARBOSA, Simone D. J. Brazilian Lyrics-Based Music Genre Classification Using a BLSTM Network. In: Proceedings of International Conference on Artificial Intelligence and Soft Computing (ICAISC). Vol. 12415, Zakopane, Poland, 2020. Doi: 10.1007/978-3-030-61401-0 49 pp. 525–534. DOI: https://doi.org/10.1007/978-3-030-61401-0_49

NEWMAN, Mark E. J. Networks: An Introduction. Oxford University Press, 2010. ISBN 978-0-19920665-0.

NOBRE, Gabriel P.; FERREIRA, Carlos H. G.; ALMEIDA, Jussara M. A hierarchical network-oriented analysis of user participation in misinformation spread on WhatsApp. Information Processing & Management. Vol. 59, no. 1, p. 102757, 2022. Doi: 10.1016/j.ipm.2021.102757 DOI: https://doi.org/10.1016/j.ipm.2021.102757

OLIVEIRA, Gabriel P.; SILVA, Mariana O.; SEUFITELLI, Danilo B.; LACERDA, Anisio; MORO, Mirella M. Detecting collaboration profiles in success-based music genre networks. In: Proceedings of Int'l Society for Music Information Retrieval Conference (ISMIR), 21, 2020, Montreal, Canada. p. 726-732.

PADRON, Marcos F.; CRUZ, Fernando W.; SILVA, Juliana R. Towards a Conceptual Model for Brazilian Popular Music Representation. In: Proceedings of Seminário de Pesquisa em Ontologias no Brasil (ONTOBRAS). São Paulo, Brazil, 2018, pp. 263–268. [Online]. Available: http://ceur-ws.org/Vol-2228/short8.pdf

PADRON, Marcos F.; CRUZ, Fernando W.; SILVA, Juliana R. Extending the IFLA Library Reference Model for a Brazilian popular music digital library. International Journal on Digital Libraries. Vol. 21, no. 3, pp. 289–306, 2020. Doi: 10.1007/s00799-020-00277-5 DOI: https://doi.org/10.1007/s00799-020-00277-5

PICHL, Martin; ZANGERLE, Eva; SPETCH, Gunther; SCHEDL, Markus. Mining Culture-Specific Music Listening Behavior from Social Media Data. In: Proceedings of IEEE International Symposium on Multimedia (ISM). Taichung, Taiwan, 2017. Doi: 10.1109/ISM.2017.35 pp. 208–215. DOI: https://doi.org/10.1109/ISM.2017.35

SCHEDL, Markus; FERWERDA, Bruce. Large-Scale Analysis of Group-Specific Music Genre Taste from Collaborative Tags. Proceedings of IEEE International Symposium on Multimedia (ISM). Taichung, Taiwan, 2017a. Doi: 10.1109/ISM.2017.95 pp. 479–482. DOI: https://doi.org/10.1109/ISM.2017.95

SCHEDL, Markus; LEMMERICH, Florian; FERWERDA, Bruce; SKOWRON, Marcin; KNEES, Peter. Indicators of Country Similarity in Terms of Music Taste, Cultural, and Socio-economic Factors. In: Proceedings of IEEE International Symposium on Multimedia (ISM). Taichung, Taiwan, 2017b. Doi: 10.1109/ISM.2017.55 pp. 308–311. DOI: https://doi.org/10.1109/ISM.2017.55

SEUFITELLI, Danilo B.; OLIVEIRA, Gabriel P.; SILVA, Mariana O.; BARBOSA, Gabriel R. G.; MELO, Bruna C.; BOTELHO, Juliana E.; MELO-GOMES, Luiza de; MORO, Mirella M. From Compact Discs to Streaming. Revista Vórtex, Curitiba, v.10, n.1, p. 1-28, April, 2022. Doi: 10.33871/23179937.2022.10.1.2 DOI: https://doi.org/10.33871/23179937.2022.10.1.2

SILVA, Mariana O.; ROCHA, Laís M.; MORO, Mirella M. Collaboration profiles and their impact on musical success. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. New York: ACM, 2019. p. 2070-2077. DOI: https://doi.org/10.1145/3297280.3297483

SILVEIRA, Bárbara; SILVA, Henrique S.; MURAI, Fabrício; SILVA, Ana Paula C. Predicting user emotional tone in mental disorder online communities. Future Generation Computer Systems. Vol. 125, pp. 641–651, 2021. Doi: 10.1016/j.future.2021.07.014 DOI: https://doi.org/10.1016/j.future.2021.07.014

UZZI, Brian; SPIRO, Jarret. Collaboration and Creativity: The Small World Problem. American Journal of Sociology, vol. 111, no. 2, pp. 447–504, 2005. Doi: 10.1086/432782 DOI: https://doi.org/10.1086/432782

VOTTER, Michael; MAYERL, Maximilian; SPETCH, Gunther; ZANGERLE, Eva. Novel Datasets for Evaluating Song Popularity Prediction Tasks. In: Proceedings of IEEE International Symposium on Multimedia (ISM). Naple, Italy, 2021. Doi: 10.1109/ISM52913.2021.00034 pp. 166–173. DOI: https://doi.org/10.1109/ISM52913.2021.00034

XU, Dongkuan; TIAN, Yingjie. A Comprehensive Survey of Clustering Algorithms. In: Annals of Data Science, vol. 2, no. 2, pp. 165–193, 2015. DOI: https://doi.org/10.1007/s40745-015-0040-1

ZANGERLE, Eva; HUBER, Ramona; VOTTER, Michael; YANG, Yi-Hsuan. Hit Song Prediction: Leveraging Low- and High-Level Audio Features. In: Proceedings of Int'l Society for Music Information Retrieval Conference (ISMIR), Delft, the Netherlands, 2019, pp. 319–326. [Online]. Available: http://archives.ismir.net/ismir2019/paper/000037.pdf

Descargas

Publicado

2023-08-03

Cómo 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

Número

Vol. 11 Núm. 2 (2023): Revista Vórtex

Sección

Artigos

Licencia

Derechos de autor 2023 Mirella M. Moro, Mariana O. Silva, Gabriel P. Oliveira, Danilo B. Seufitelli

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.

Los autores conservan los derechos de autor y conceden a la revista el derecho de primera publicación, con el trabajo simultáneamente bajo la licencia Creative Commons Attribution Licence que permite compartir el trabajo con reconocimiento de autoría y publicación inicial en esta revista.

Métrica