Analyzing the temporal relation between virality and success in the Brazilian music market
Resumo
Content virality on social media platforms is essential to modern digital culture. In music, viral songs often gain widespread attention through catchy melodies, relatable lyrics, and captivating visuals. Indeed, social platforms have reshaped music consumption, with viral trends often leading to mainstream success. This study investigates the relationship between music virality and success in Brazil by analyzing their evolution in streaming platforms over time. Through correlation and Granger Causality analyses, we explore the dynamics between these facets of music popularity. Our results show that virality can be used to forecast future success and vice versa, but this cannot be generalized to all songs. Such findings reinforce the differences between the concepts of virality and success besides their symbiotic relationship driven by social platforms.
Referências
Barbosa, G. R. G. et al. (2021). Hot Streaks in the Brazilian Music Market: A Comparison Between Physical and Digital Eras. In SBCM, pages 152–159, Recife, Brazil. SBC.
Bryan, N. J. and Wang, G. (2011). Musical influence network analysis and rank of sample-based music. In ISMIR, pages 329–334, Miami, USA. ISMIR.
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Academic press.
Dhanaraj, R. and Logan, B. (2005). Automatic prediction of hit songs. In ISMIR, pages 488–491, London, UK. ISMIR.
Fink, L. K. et al. (2021). Viral tunes: changes in musical behaviours and interest in coronamusic predict socio-emotional coping during COVID-19 lockdown. Humanities and Social Sciences Communications, 8(1).
Fuller, W. A. (2009). Introduction to statistical time series. John Wiley & Sons.
Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society, pages 424–438.
Guerini, M. et al. (2011). Exploring text virality in social networks. In ICWSM, pages 506–509, Barcelona, Spain. The AAAI Press.
Kahl, C. and Albers, A. (2013). How to unleash the virus social networks as a host for viral music marketing. In CBI, pages 47–54, Vienna, Austria. IEEE Computer Society.
Kong, Q. et al. (2018). Will this video go viral: Explaining and predicting the popularity of youtube videos. In WWW (Companion Volume), pages 175–178, Lyon, France. ACM.
Krijestorac, H. et al. (2020). Cross-platform spillover effects in consumption of viral content: A quasi-experimental analysis using synthetic controls. Inf. Syst. Res., 31(2):449–472.
Kwiatkowski, D. et al. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of econometrics, 54(1-3):159–178.
Ling, C. et al. (2022). Slapping cats, bopping heads, and oreo shakes: Understanding indicators of virality in tiktok short videos. In WebSci, pages 164–173, Barcelona, Spain. ACM.
Oliveira, G. P. et al. (2023). Hot streaks in the music industry: identifying and characterizing above-average success periods in artists’ careers. Scientometrics, 128(11):6029–6046.
Oliveira, G. P. et al. (2024). What makes a viral song? Unraveling music virality factors. In WebSci, pages 181–190, New York, NY, USA. ACM.
Pereira, F. S. F. et al. (2018). That’s my jam! uma análise temporal sobre a evolução das preferências dos usuários em uma rede social de músicas. In BraSNAM. SBC.
Ramos, L. R. et al. (2020). Geração semiautomática de valores de referência para identificação de obstruções em lingotamento contínuo. In SEMISH, pages 116–127, Cuiabá, Brazil. SBC.
Rossi, D. F. et al. (2023). Identificação de estáticas em poços de petróleo utilizando motifs. In SEMISH, pages 308–319, João Pessoa, Brazil. SBC.
Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, pages 461–464.
Seabold, S. and Perktold, J. (2010). Statsmodels: Econometric and Statistical Modeling with Python. In SciPy, pages 92–96, Austin, USA. scipy.org.
Seufitelli, D. B. et al. (2023a). Hit song science: a comprehensive survey and research directions. J. New Music Res., 52(1):41–72.
Seufitelli, D. B. et al. (2023b). MGD+: An Enhanced Music Genre Dataset with Success-based Networks. In Dataset Showcase Workshop, pages 36–47. SBC.
Silva, M. O. and Moro, M. M. (2019). Causality analysis between collaboration profiles and musical success. In WebMedia, pages 369–376, Rio de Janeiro, Brazil. ACM.