Hot Streaks in the Brazilian Music Market: A Comparison Between Physical and Digital Eras
Resumo
Consuming music through streams has made huge volumes of data available. We collect a part of such data and perform cross-era comparative analyses between physical and digital media for successful artists within the music market in Brazil. Given an artist’s career, we focus on hot streak periods defined as high-impact bursts occurring in sequence. Specifically, we construct artists’ success time series to detect and characterize hot streak periods for both physical and digital eras. Then, we assess their features, analyze them in the genre scale, and perform a cluster analysis to identify groups of artists with distinct success levels. For both physical and digital eras, we find the same clusters: Spike Hit Artists, Big Hit Artists, and Top Hit Artists. Our insights shed light on significant changes in the dynamics of the music industry over the years, by identifying the core of each era.
Palavras-chave:
Music Information Retrieval
Referências
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Vítor Shinohara, Juliano Foleiss, and Tiago Tavares. Comparing meta-classifiers for automatic music genre classification. In SBCM, pages 131–135, 2019.
Carlos Soares Araujo, Marco Cristo, and Rafael Giusti. Predicting music popularity on streaming platforms. In SBCM, pages 141–148, 2019.
D. Martín-Gutiérrez et al. A multimodal end-to-end deep learning architecture for music popularity prediction. IEEE Access, 8:39361–39374, 2020.
R Sinatra, D Wang, P Deville, C Song, and A-L Barabási. Quantifying the evolution of individual scientific impact. Science, 354(6312), 2016.
Kiran Garimella and Robert West. Hot streaks on social media. In ICWSM, pages 170–180, 2019.
Lu Liu et al. Hot streaks in artistic, cultural, and scientific careers. Nature, 559(7714):396–399, 2018.
Milan Janosov, Federico Battiston, and Roberta Sinatra. Success and luck in creative careers. EPJ Data Sci., 9(1):9, 2020.
Leonardo De Marchi and João Martins Ladeira. Digitization of music and audio-visual industries in brazil: new actors and the challenges to cultural diversity. Cahiers d'Outre-Mer, 71(277):67–86, January 2018.
Marcelo Kischinhevsky, Eduardo Vicente, and Leonardo De Marchi. Em busca da música infinita: os serviços de streaming e os conflitos de interesse no mercado de conteúdos digitais. Fronteiras-estudos midiáticos, 17(3):302–311, 2015.
Joel Waldfogel. How digitization has created a golden age of music, movies, books, and television. Journal of economic perspectives, 31(3):195–214, 2017.
Mariana O. Silva, Laís M. Rocha, and Mirella M. Moro. Collaboration Profiles and Their Impact on Musical Success. In ACM/SIGAPP SAC, pages 2070–2077, 2019.
Gabriel P. Oliveira et al. Detecting collaboration profiles in success-based music genre networks. In ISMIR, pages 726–732, 2020.
R Borges and Marcelo Queiroz. A probabilistic model for recommending music based on acoustic features and social data. In SBCM, pages 7–12, 2017.
R de Araújo Lima et al. Brazilian lyrics-based music genre classification using a BLSTM network. In ICAIS, 2020.
Darryll Hendricks, Jayendu Patel, and Richard Zeckhauser. Hot hands in mutual funds: Short-run persistence of relative performance, 1974–1988. The Journal of finance, 48(1):93–130, 1993.
Matthew Rabin and Dimitri Vayanos. The gambler’s and hot-hand fallacies: Theory and applications. The Review of Economic Studies, 77(2):730–778, 2010.
Markus Raab, Bartosz Gula, and Gerd Gigerenzer. The hot hand exists in volleyball and is used for allocation decisions. Journal of Experimental Psychology: Applied, 18(1):81, 2012.
Gabriel B. Vaz de Melo, Ana F. Machado, and Lucas R. de Carvalho. Music consumption in Brazil: an analysis of streaming reproductions. PragMATIZES - Revista Latino-Americana de Estudos em Cultura, 10(19):141, 2020.
Eamonn J. Keogh and Michael J. Pazzani. Scaling up dynamic time warping for datamining applications. In SIGKDD, pages 285–289. ACM, 2000.
R Tavenard et al. Tslearn a machine learning toolkit for time series data. J.Mach.Learn.Res, 21:118:1–118:6, 2020.
Purnima Bholowalia and Arvind Kumar. Ebk-means: A clustering technique based on elbow method and k-means in wsn. Int’l J. of Computer Applications, 105(9), 2014.
Vítor Shinohara, Juliano Foleiss, and Tiago Tavares. Comparing meta-classifiers for automatic music genre classification. In SBCM, pages 131–135, 2019.
Carlos Soares Araujo, Marco Cristo, and Rafael Giusti. Predicting music popularity on streaming platforms. In SBCM, pages 141–148, 2019.
D. Martín-Gutiérrez et al. A multimodal end-to-end deep learning architecture for music popularity prediction. IEEE Access, 8:39361–39374, 2020.
R Sinatra, D Wang, P Deville, C Song, and A-L Barabási. Quantifying the evolution of individual scientific impact. Science, 354(6312), 2016.
Kiran Garimella and Robert West. Hot streaks on social media. In ICWSM, pages 170–180, 2019.
Lu Liu et al. Hot streaks in artistic, cultural, and scientific careers. Nature, 559(7714):396–399, 2018.
Milan Janosov, Federico Battiston, and Roberta Sinatra. Success and luck in creative careers. EPJ Data Sci., 9(1):9, 2020.
Leonardo De Marchi and João Martins Ladeira. Digitization of music and audio-visual industries in brazil: new actors and the challenges to cultural diversity. Cahiers d'Outre-Mer, 71(277):67–86, January 2018.
Marcelo Kischinhevsky, Eduardo Vicente, and Leonardo De Marchi. Em busca da música infinita: os serviços de streaming e os conflitos de interesse no mercado de conteúdos digitais. Fronteiras-estudos midiáticos, 17(3):302–311, 2015.
Joel Waldfogel. How digitization has created a golden age of music, movies, books, and television. Journal of economic perspectives, 31(3):195–214, 2017.
Mariana O. Silva, Laís M. Rocha, and Mirella M. Moro. Collaboration Profiles and Their Impact on Musical Success. In ACM/SIGAPP SAC, pages 2070–2077, 2019.
Gabriel P. Oliveira et al. Detecting collaboration profiles in success-based music genre networks. In ISMIR, pages 726–732, 2020.
R Borges and Marcelo Queiroz. A probabilistic model for recommending music based on acoustic features and social data. In SBCM, pages 7–12, 2017.
R de Araújo Lima et al. Brazilian lyrics-based music genre classification using a BLSTM network. In ICAIS, 2020.
Darryll Hendricks, Jayendu Patel, and Richard Zeckhauser. Hot hands in mutual funds: Short-run persistence of relative performance, 1974–1988. The Journal of finance, 48(1):93–130, 1993.
Matthew Rabin and Dimitri Vayanos. The gambler’s and hot-hand fallacies: Theory and applications. The Review of Economic Studies, 77(2):730–778, 2010.
Markus Raab, Bartosz Gula, and Gerd Gigerenzer. The hot hand exists in volleyball and is used for allocation decisions. Journal of Experimental Psychology: Applied, 18(1):81, 2012.
Gabriel B. Vaz de Melo, Ana F. Machado, and Lucas R. de Carvalho. Music consumption in Brazil: an analysis of streaming reproductions. PragMATIZES - Revista Latino-Americana de Estudos em Cultura, 10(19):141, 2020.
Eamonn J. Keogh and Michael J. Pazzani. Scaling up dynamic time warping for datamining applications. In SIGKDD, pages 285–289. ACM, 2000.
R Tavenard et al. Tslearn a machine learning toolkit for time series data. J.Mach.Learn.Res, 21:118:1–118:6, 2020.
Purnima Bholowalia and Arvind Kumar. Ebk-means: A clustering technique based on elbow method and k-means in wsn. Int’l J. of Computer Applications, 105(9), 2014.
Publicado
24/10/2021
Como Citar
BARBOSA, Gabriel R. G.; MELO, Bruna C.; OLIVEIRA, Gabriel P.; SILVA, Mariana O.; SEUFITELLI, Danilo B.; MORO, Mirella M..
Hot Streaks in the Brazilian Music Market: A Comparison Between Physical and Digital Eras. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO MUSICAL (SBCM), 18. , 2021, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 152-159.
DOI: https://doi.org/10.5753/sbcm.2021.19440.
