Exceptional Collaboration Patterns in Music Genre Networks

• Gabriel P. Oliveira UFMG
• Mirella M. Moro UFMG

DOI: https://doi.org/10.5753/brasnam.2023.230516

Resumo

Music is one of the world’s most important cultural forms, and also one of the most dynamic. Such a dynamic nature can directly influence artists’ careers and reflect their success. In this work, we combine social networks and data mining techniques to analyze musical success from a genre-oriented perspective. Our goal is to mine exceptional collaboration patterns in success-based genre networks where the success deviates from the average. We conduct our analyses for global and eight regional markets, and the results show that each market has specific patterns of genre connections in which success is above average. Hence, our findings serve as a first step in developing strategies to promote future song releases across the world.

Referências

Araujo, C. V. et al. (2017). Predicting music success based on users’ comments on online social networks. In WebMedia, pages 149–156, Brazil.

Bryan, N. J. and Wang, G. (2011). Musical influence network analysis and rank of sample-based music. In ISMIR, pages 329–334, Miami, USA.

Cosimato, A., Prisco, R. D., Guarino, A., Malandrino, D., Lettieri, N., Sorrentino, G., and Zaccagnino, R. (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.

Iloga, S., Romain, O., and Tchuente, M. (2018). A sequential pattern mining approach to design taxonomies for hierarchical music genre recognition. Pattern Anal. Appl., 21(2):363–380.

Jorge, C. C., Atzmueller, M., Heravi, B. M., Gibson, J. L., Rebelo de Sá, C., and Rossetti, R. J. F. (2019). Mining exceptional social behaviour. In EPIA (2), volume 11805 of Lecture Notes in Computer Science, pages 460–472. Springer.

Klösgen, W. and Zytkow, J. M. (2002). Handbook of data mining and knowledge discovery. Oxford University Press, Inc.

Lemmerich, F. and Becker, M. (2018). pysubgroup: Easy-to-use subgroup discovery in python. In ECML/PKDD (3), volume 11053 of LNCS, pages 658–662. Springer.

Mayerl, M., Vötter, M., Specht, G., and Zangerle, E. (2023). Pairwise learning to rank for hit song prediction. In BTW, volume P-331 of LNI, pages 555–565.

Mondelli, M. L. B., Gadelha Jr., L. M. R., and Ziviani, A. (2018). O que os países escutam: Analisando a rede de gêneros musicais ao redor do mundo. In BraSNAM, Natal.

Oliveira, G. P., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2020). Detecting collaboration profiles in success-based music genre networks. In ISMIR, pages 726–732.

Pachet, F. (2012). Hit song science. In Tao Li, Mitsunori Ogihara, G. T., editor, Music Data Mining, chapter 10, pages 305–326. CRC Press, New York, NY, USA.

Pachet, F. and Roy, P. (2008). Hit song science is not yet a science. In ISMIR, pages 355–360, Philadelphia, USA.

Pereira, F. S. F., Linhares, C. D. G., Ponciano, J. R., Gama, J., de Amo, S., and Oliveira, G. M. B. (2018). That’s my jam! uma análise temporal sobre a evolução das preferências dos usuários em uma rede social de músicas. In BraSNAM, Natal. SBC.

Rebelo de Sá, C., Duivesteijn, W., Azevedo, P. J., Jorge, A. M., Soares, C., and Knobbe, A. J. (2018). Discovering a taste for the unusual: exceptional models for preference mining. Mach. Learn., 107(11):1775–1807.

Ren, J. and Kauffman, R. J. (2017). Understanding music track popularity in a social network. In 25th European Conference on Information Systems, pages 374–388, Atlanta, GA, USA. AIS.

Rompré, L., Biskri, I., and Meunier, J. (2017). Using association rules mining for retrieving genre-specific music files. In FLAIRS Conference, pages 706–711. AAAI Press.

Shin, S. and Park, J. (2018). On-chart success dynamics of popular songs. Advances in Complex Systems, 21(3-4):1850008.

Siddiquee, M. M. R., Rahman, M. S., Chowdhury, S. U. I., and Rahman, R. M. (2016). Association rule mining and audio signal processing for music discovery and recommendation. Int. J. Softw. Innov., 4(2):71–87.

Silva, M. O., Oliveira, G. P., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2022). Collaboration as a driving factor for hit song classification. In WebMedia, pages 66–74, Curitiba, Brazil.

Tsiara, E. and Tjortjis, C. (2020). Using twitter to predict chart position for songs. In IFIP Artificial Intelligence Applications and Innovations, pages 62–72, Neos Marmaras, Greece.

Zhao, M., Harvey, M., Cameron, D., Hopfgartner, F., and Gillet, V. J. (2023). An analysis of classification approaches for hit song prediction using engineered metadata features with lyrics and audio features. In iConference (1), volume 13971 of Lecture Notes in Computer Science, pages 303–311.