Analyses of Musical Success based on Time, Genre and Collaboration
Resumo
Music holds a significant position in global culture, as it is one of the world's most important and dynamic cultural forms. With the vast amount of music-related data available on the Web, new opportunities emerge for extracting knowledge and benefiting different music segments. In this work, we perform a data-driven analysis to investigate musical success from a genre-oriented perspective. Specifically, we model both artist and genre success timelines to detect and predict continuous periods with higher impact. We also build success-based genre collaboration networks to detect collaboration profiles directly related to success. Furthermore, we use data mining techniques to uncover exceptional genre patterns in the networks where the success deviates from the average. Our findings show that studying genre collaboration is a powerful way to assess musical success by describing similar behaviors within collaborative songs. Overall, our work contributes to both the academy and the music industry, as we shed light on the underlying factors of the science behind musical success.
Palavras-chave:
Hit song science, Music information retrieval, Musical genres, Complex networks, Data science, Data mining
Referências
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Askin, N. and Mauskapf, M. (2017). What makes popular culture popular? product features and optimal differentiation in music. Amer. Sociolog. Rev., 82(5):910–944.
Barbosa, G. R. G., Melo, B. C., Oliveira, G. P., Silva, M. O., Seufitelli, D. B., and Moro, M. M. (2021). Hot Streaks in the Brazilian Music Market: A Comparison Between Physical and Digital Eras. In SBCM. SBC.
Bryan, N. J. andWang, G. (2011). Musical influence network analysis and rank of samplebased music. In ISMIR, pages 329–334.
Cosimato, A. et al. (2019). The conundrum of success in music: Playing it or talking about it? IEEE Access, 7:123289–123298.
Dhanaraj, R. and Logan, B. (2005). Automatic prediction of hit songs. In ISMIR, pages 488–491, London, UK. ISMIR.
Garimella, K. and West, R. (2019). Hot streaks on social media. In ICWSM, pages 170–180. AAAI Press.
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Keogh, E. J. and Pazzani, M. J. (2000). Scaling up dynamic time warping for data mining applications. In KDD, pages 285–289.
Klösgen, W. and Zytkow, J. M. (2002). Handbook of data mining and knowledge discovery. Oxford University Press, Inc.
Lundberg, S. M. and Lee, S. (2017). A unified approach to interpreting model predictions. In NIPS, pages 4765–4774.
Moura, A. F. C., Oliveira, G. P., Brandão, M. A., and Moro, M. M. (2020). Classification and persistence analysis of tie strength on github. In Anais Est. WebMedia, pages 41–44. SBC.
Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Botelho, J. E., Silva, M. O., Seufitelli, D. B., and Moro, M. M. (2022a). Musical Success in the United States and Brazil: Novel Datasets and Temporal Analyses. JIDM.
Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2021a). MUHSIC: Music-oriented Hot Streak Information Collection. https://doi.org/10.5281/zenodo.4779003.
Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Silva, M. O., Seufitelli, D. B., and Moro, M. M. (2021b). MUHSIC: An open dataset with temporal musical success information. In SBBD DSW, pages 65–76. SBC.
Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Silva, M. O., Seufitelli, D. B., and Moro, M. M. (2023a). Hot streaks in the music industry: Identifying and characterizing above-average success periods in artists’ careers. Scientometrics. [under review].
Oliveira, G. P., Lacerda, A., and Moro, M. M. (2020a). Musical genre analysis over dynamic success-based networks. In SBBD WTDBD. SBC.
Oliveira, G. P., Lacerda, A., and Moro, M. M. (2022b). Analyses of musical success based on time, genre and collaboration. In Anais do XXXV CTD. SBC.
Oliveira, G. P. and Moro, M. M. (2023a). Exceptional collaboration patterns in music genre networks. In BraSNAM. SBC.
Oliveira, G. P. and Moro, M. M. (2023b). Mining exceptional genre patterns on hit songs. In KDMiLe. SBC.
Oliveira, G. P., Moura, A. F. C., Batista, N. A., Brandao, M. A., Hora, A., and Moro, M. M. (2023b). How do developers collaborate? investigating github heterogeneous networks. Software Quality Journal, 31:211–241.
Oliveira, G. P., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2020b). Detecting collaboration profiles in success-based music genre networks. In ISMIR, pages 726–732.
Oliveira, G. P., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2020c). MGD: Music Genre Dataset. https://doi.org/10.5281/zenodo.4778563.
Paula, B. C. M., Oliveira, G. P., and Moro, M. M. (2022). Mood Analysis during the COVID-19 Pandemic in Brazil through Music. In WebMedia CTIC, pages 53–56, Porto Alegre, RS, Brasil. SBC.
Pimentel, J. F., Oliveira, G. P., Silva, M. O., Seufitelli, D. B., and Moro, M. M. (2021). Ciência de dados com reprodutibilidade usando jupyter. In Jornada de Atualização em Informática 2021, chapter 1, pages 13–62. SBC.
Ren, J. and Kauffman, R. J. (2017). Understanding music track popularity in a social network. In Euro. Conf. Information Systems, pages 374–388, Atlanta, USA. AIS.
Seufitelli, D. B., Oliveira, G. P., Silva, M. O., Barbosa, G. R. G., Melo, B. C., Botelho, J. E., Melo-Gomes, L., and Moro, M. M. (2022). From compact discs to streaming: A comparison of eras within the brazilian market. Vortex Music Journal, 10(1).
Seufitelli, D. B., Oliveira, G. P., Silva, M. O., Scofield, C., and Moro, M. M. (2023). Hit song science: A comprehensive survey and research directions. JNMR. [under review].
Shin, S. and Park, J. (2018). On-chart success dynamics of popular songs. Advances in Complex Systems, 21(3-4):1850008.
Silva, M. O. et al. (2019). Collaboration Profiles and Their Impact on Musical Success. In Procs. of ACM/SIGAPP SAC, pages 2070–2077, Limassol, Cyprus.
Silva, M. O. and Moro, M. M. (2019). Causality analysis between collaboration profiles and musical success. In WebMedia, pages 369–376. ACM.
Silva, M. O., Oliveira, G. P., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2022a). Collaboration as a driving factor for hit song classification. In WebMedia.
Silva, M. O., Oliveira, G. P., Seufitelli, D. B., and Moro, M. M. (2023). Temporal Success Analysis in Music Collaboration Networks: Brazilian versus Global Scenarios. Vortex Music Journal. [accepted for publication].
Silva, M. O., Scofield, C., de Melo-Gomes, L., Botelho, J. E., Oliveira, G. P., Seufitelli, D. B., and Moro, M. M. (2022b). Brazilian reading preferences in goodreads: Crossstate and cross-region analyses. iSys, 15(1).
Silva, M. O., Scofield, C., Oliveira, G. P., Seufitelli, D. B., and Moro, M. M. (2021). Exploring brazilian cultural identity through reading preferences. In BraSNAM, pages 115–126.
Sinatra, R. et al. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312).
Suh, M. M. et al. (2021). AI as social glue: Uncovering the roles of deep generative AI during social music composition. In CHI, pages 582:1–582:11. ACM.
Zaki, M. J. and Meira Jr., W. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press.
Askin, N. and Mauskapf, M. (2017). What makes popular culture popular? product features and optimal differentiation in music. Amer. Sociolog. Rev., 82(5):910–944.
Barbosa, G. R. G., Melo, B. C., Oliveira, G. P., Silva, M. O., Seufitelli, D. B., and Moro, M. M. (2021). Hot Streaks in the Brazilian Music Market: A Comparison Between Physical and Digital Eras. In SBCM. SBC.
Bryan, N. J. andWang, G. (2011). Musical influence network analysis and rank of samplebased music. In ISMIR, pages 329–334.
Cosimato, A. et al. (2019). The conundrum of success in music: Playing it or talking about it? IEEE Access, 7:123289–123298.
Dhanaraj, R. and Logan, B. (2005). Automatic prediction of hit songs. In ISMIR, pages 488–491, London, UK. ISMIR.
Garimella, K. and West, R. (2019). Hot streaks on social media. In ICWSM, pages 170–180. AAAI Press.
Janosov, M. et al. (2020). Success and luck in creative careers. EPJ Data Sci., 9(1):9.
Keogh, E. J. and Pazzani, M. J. (2000). Scaling up dynamic time warping for data mining applications. In KDD, pages 285–289.
Klösgen, W. and Zytkow, J. M. (2002). Handbook of data mining and knowledge discovery. Oxford University Press, Inc.
Lundberg, S. M. and Lee, S. (2017). A unified approach to interpreting model predictions. In NIPS, pages 4765–4774.
Moura, A. F. C., Oliveira, G. P., Brandão, M. A., and Moro, M. M. (2020). Classification and persistence analysis of tie strength on github. In Anais Est. WebMedia, pages 41–44. SBC.
Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Botelho, J. E., Silva, M. O., Seufitelli, D. B., and Moro, M. M. (2022a). Musical Success in the United States and Brazil: Novel Datasets and Temporal Analyses. JIDM.
Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2021a). MUHSIC: Music-oriented Hot Streak Information Collection. https://doi.org/10.5281/zenodo.4779003.
Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Silva, M. O., Seufitelli, D. B., and Moro, M. M. (2021b). MUHSIC: An open dataset with temporal musical success information. In SBBD DSW, pages 65–76. SBC.
Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Silva, M. O., Seufitelli, D. B., and Moro, M. M. (2023a). Hot streaks in the music industry: Identifying and characterizing above-average success periods in artists’ careers. Scientometrics. [under review].
Oliveira, G. P., Lacerda, A., and Moro, M. M. (2020a). Musical genre analysis over dynamic success-based networks. In SBBD WTDBD. SBC.
Oliveira, G. P., Lacerda, A., and Moro, M. M. (2022b). Analyses of musical success based on time, genre and collaboration. In Anais do XXXV CTD. SBC.
Oliveira, G. P. and Moro, M. M. (2023a). Exceptional collaboration patterns in music genre networks. In BraSNAM. SBC.
Oliveira, G. P. and Moro, M. M. (2023b). Mining exceptional genre patterns on hit songs. In KDMiLe. SBC.
Oliveira, G. P., Moura, A. F. C., Batista, N. A., Brandao, M. A., Hora, A., and Moro, M. M. (2023b). How do developers collaborate? investigating github heterogeneous networks. Software Quality Journal, 31:211–241.
Oliveira, G. P., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2020b). Detecting collaboration profiles in success-based music genre networks. In ISMIR, pages 726–732.
Oliveira, G. P., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2020c). MGD: Music Genre Dataset. https://doi.org/10.5281/zenodo.4778563.
Paula, B. C. M., Oliveira, G. P., and Moro, M. M. (2022). Mood Analysis during the COVID-19 Pandemic in Brazil through Music. In WebMedia CTIC, pages 53–56, Porto Alegre, RS, Brasil. SBC.
Pimentel, J. F., Oliveira, G. P., Silva, M. O., Seufitelli, D. B., and Moro, M. M. (2021). Ciência de dados com reprodutibilidade usando jupyter. In Jornada de Atualização em Informática 2021, chapter 1, pages 13–62. SBC.
Ren, J. and Kauffman, R. J. (2017). Understanding music track popularity in a social network. In Euro. Conf. Information Systems, pages 374–388, Atlanta, USA. AIS.
Seufitelli, D. B., Oliveira, G. P., Silva, M. O., Barbosa, G. R. G., Melo, B. C., Botelho, J. E., Melo-Gomes, L., and Moro, M. M. (2022). From compact discs to streaming: A comparison of eras within the brazilian market. Vortex Music Journal, 10(1).
Seufitelli, D. B., Oliveira, G. P., Silva, M. O., Scofield, C., and Moro, M. M. (2023). Hit song science: A comprehensive survey and research directions. JNMR. [under review].
Shin, S. and Park, J. (2018). On-chart success dynamics of popular songs. Advances in Complex Systems, 21(3-4):1850008.
Silva, M. O. et al. (2019). Collaboration Profiles and Their Impact on Musical Success. In Procs. of ACM/SIGAPP SAC, pages 2070–2077, Limassol, Cyprus.
Silva, M. O. and Moro, M. M. (2019). Causality analysis between collaboration profiles and musical success. In WebMedia, pages 369–376. ACM.
Silva, M. O., Oliveira, G. P., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2022a). Collaboration as a driving factor for hit song classification. In WebMedia.
Silva, M. O., Oliveira, G. P., Seufitelli, D. B., and Moro, M. M. (2023). Temporal Success Analysis in Music Collaboration Networks: Brazilian versus Global Scenarios. Vortex Music Journal. [accepted for publication].
Silva, M. O., Scofield, C., de Melo-Gomes, L., Botelho, J. E., Oliveira, G. P., Seufitelli, D. B., and Moro, M. M. (2022b). Brazilian reading preferences in goodreads: Crossstate and cross-region analyses. iSys, 15(1).
Silva, M. O., Scofield, C., Oliveira, G. P., Seufitelli, D. B., and Moro, M. M. (2021). Exploring brazilian cultural identity through reading preferences. In BraSNAM, pages 115–126.
Sinatra, R. et al. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312).
Suh, M. M. et al. (2021). AI as social glue: Uncovering the roles of deep generative AI during social music composition. In CHI, pages 582:1–582:11. ACM.
Zaki, M. J. and Meira Jr., W. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press.
Publicado
25/09/2023
Como Citar
OLIVEIRA, Gabriel P.; LACERDA, Anisio; MORO, Mirella M..
Analyses of Musical Success based on Time, Genre and Collaboration. In: CONCURSO DE TESES E DISSERTAÇÕES (CTDBD) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 38. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 240-254.
DOI: https://doi.org/10.5753/sbbd_estendido.2023.232874.
