A quantitative comparison of viral and hit songs in the Brazilian music market

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Authors

DOI:

https://doi.org/10.33871/vortex.2024.12.8727

Keywords:

song virality, musical success, quantitative analysis, Brazil

Abstract

It is common for songs to go viral on streaming platforms and social media, but not all viral songs become hits. In this context, we aim to discover what differs viral from hit songs beyond their definition. We do so by using a quantitative methodology over charts in the Brazilian market. We compare hit and viral songs regarding their intrinsic and extrinsic characteristics, and our results reveal significant differences between them. Features such as music genres, lyrics topics, and emotions emerge as crucial elements to distinguishing such songs within the Brazilian context. Furthermore, temporal features indicate differences in the diffusion processes between hits and virals. Overall, this study offers insights into music consumption in Brazil, revealing the connection between song features and their success and virality on streaming platforms.

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Author Biographies

Gabriel 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, Brazil. 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 on collaborative domains.

Ana Paula Couto da Silva, Universidade Federal de Minas Gerais

Ana Paula Couto da Silva earned her PhD degree in Computer and System Engineering from the Federal University of Rio de Janeiro (2006). She was with IRISA-Rennes in 2005 and 2007, and with Politecnico di Torino in 2008 (as post-doc), 2016 (as visiting professor) and 2018 (as visiting researcher). She is with the Computer Science Department at the Federal University of Minas Gerais since 2013 as associate professor. She has productivity in research scholarship (Bolsa PQ - CNPq) level 2. Her areas of interest are in the field of network science, social computing and analysis of computer systems and green networking.

Mirella Moro, Universidade Federal de Minas Gerais

Mirella M. Moro, PhD, works in the Computer Science Department at Universidade Federal de Minas Gerais, Brazil. Her research interests include data-driven research, computing on culture, gender diversity, and education in Computer Science. She is also an advocate for increasing women participation in Computing and Technology and coordinates the Digital Girls Program at the Brazilian Computer Society.

References

ABEL, Fabian; DIAZ-AVILES, Ernesto; HENZE, Nicola; KRAUSE, Daniel; SIEHNDEL, Patrick. Analyzing the Blogosphere for Predicting the Success of Music and Movie Products. In: INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), 2010, Odense, Denmark. Proceedings [...]. [S. l.]: IEEE, 2010. pp. 276–280. DOI: 10.1109/ASONAM.2010.50. DOI: https://doi.org/10.1109/ASONAM.2010.50

ARAUJO, Carlos Soares; CRISTO, Marco; GIUSTI, Rafael. Predicting Music Popularity on Streaming Platforms. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO MUSICAL (SBCM), 17, 2019, São João del-Rei, Brazil. Anais [...]. Porto Alegre: SBC, 2019. pp. 141-148. DOI: 10.5753/sbcm.2019.10436. DOI: https://doi.org/10.5753/sbcm.2019.10436

BASTOS, Hemilly; GIUNTI, Débora Moreira; BENVINDO, Larissa; NASCIMENTO, Alexandre; INOCÊNCIO, Luana. Trends no TikTok e sua influência no streaming musical: os casos Doja Cat e Olivia Rodrigo. In: CONGRESSO BRASILEIRO DE CIÊNCIAS DA COMUNICAÇÃO, 2021, Evento virtual. Anais [..]. [S. l.]: INTERCOM, 2021. pp. 1-15.

BISCHOFF, Kerstin; FIRAN, Claudiu S.; GEORGESCU, Mihai; NEJDL, Wolfgang; PAIU, Raluca. Social Knowledge-Driven Music Hit Prediction. In: INTERNATIONAL CONFERENCE ON ADVANCED DATA MINING AND APPLICATIONS (ADMA), 2009, Beijing, China. Proceedings [...]. New York: Springer, 2009. pp. 43-54. DOI: 10.1007/978-3-642-03348-3_8. DOI: https://doi.org/10.1007/978-3-642-03348-3_8

BLEI, David M.; NG, Andrew Y.; JORDAN, Michael I. Latent Dirichlet Allocation. Journal of Machine Learning Research, [S. l.], v. 3, pp. 993-1022, 2003.

COSIMATO, Alberto; DE PRISCO, Roberto; GUARINO, Alfonso; MALANDRINO, Delfina; LETTIERI, Nicola; SORRENTINO, Giuseppe; ZACCAGNINO, Rocco. The Conundrum of Success in Music: Playing it or Talking About it?. IEEE Access, [S. l.], v. 7, pp. 123289-123298, 2019. DOI: 10.1109/ACCESS.2019.2937743. DOI: https://doi.org/10.1109/ACCESS.2019.2937743

DHANARAJ, Ruth; LOGAN, Beth. Automatic Prediction of Hit Songs. In: INTERNATIONAL SOCIETY FOR MUSIC INFORMATION RETRIEVAL CONFERENCE (ISMIR), 2005, London, UK. Proceedings [...]. [S. l.]: ISMIR, 2005. pp. 488-491.

DUMAN, Deniz; NETO, Pedro; MAVROLAMPADOS, Anastasios; TOIVIAINEN, Petri; LUCK Geoff. Music we move to: Spotify audio features and reasons for listening. PLoS ONE, [S. l.], v. 17, n. 9, p. e0275228, 2022. DOI: 10.1371/journal.pone.0275228 DOI: https://doi.org/10.1371/journal.pone.0275228

GUERINI, Marco; STRAPPARAVA, Carlo; ÖZBAL, Gözde. Exploring Text Virality in Social Networks. In: INTERNATIONAL AAAI CONFERENCE ON WEB AND SOCIAL MEDIA (ICWSM), 5, 2011, Barcelona, Spain. Proceedings [...]. [S. l.]: The AAAI Press, 2011. pp. 506-509. DOI: 10.1609/icwsm.v5i1.14169. DOI: https://doi.org/10.1609/icwsm.v5i1.14169

JIANG, Lu; MIAO, Yajie; YANG, Yi; LAN, Zhen-Zhong; HAUPTMANN, Alexander G. Viral Video Style: A Closer Look at Viral Videos on YouTube. In: INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL (ICMR), 2014, Glasgow, UK. Proceedings [...]. New York: ACM, 2014. pp. 193-200. DOI: 10.1145/2578726.2578754. DOI: https://doi.org/10.1145/2578726.2578754

KONG, Quyu; RIZOIU, Marian-Andrei; WU, Siqi; XIE, Lexing. Will This Video Go Viral: Explaining and Predicting the Popularity of YouTube Videos. In: THE WEB CONFERENCE (WWW), 2018, Lyon, France. Companion Proceedings [...]. New York: ACM, 2018. pp. 175-178. DOI: 10.1145/3184558.3186972. DOI: https://doi.org/10.1145/3184558.3186972

KRIJESTORAC, Haris; GARG, Rajiv; MAHAJAN, Vijay. Cross-Platform Spillover Effects in Consumption of Viral Content: A Quasi-Experimental Analysis Using Synthetic Controls. Information Systems Research, [S. l.], v. 31, n. 2, pp. 449-472, 2020. DOI: 10.1287/isre.2019.0897. DOI: https://doi.org/10.1287/isre.2019.0897

INTERNATIONAL FEDERATION OF THE PHONOGRAPHIC INDUSTRY. Engaging with music. [S. l.], 2023. Disponível em: <https://ifpi.org/wp-content/uploads/2023/12/IFPI-Engaging-With-Music-2023_full-report.pdf>. Acesso em: 19 jun. 2024.

LE COMPTE, Daniel; KLUG, Daniel. "It's Viral!" - A Study of the Behaviors, Practices, and Motivations of TikTok Users and Social Activism. In: ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING (CSCW), 2021, Virtual event. Companion Proceedings [...]. New York: ACM, 2021. pp. 108-111. DOI: 10.1145/3462204.3481741. DOI: https://doi.org/10.1145/3462204.3481741

LING, Chen; DE CRISTOFARO, Emiliano; STRINGHINI, Gianluca. Slapping Cats, Bopping Heads, and Oreo Shakes: Understanding Indicators of Virality in TikTok Short Videos. In: ACM WEB SCIENCE CONFERENCE (WEBSCI), 2022, Barcelona, Spain. Proceedings [...]. New York: ACM, 2022. pp. 164-173. DOI: 10.1145/3501247.3531551. DOI: https://doi.org/10.1145/3501247.3531551

MANN, Henry B.; WHITNEY, Donald R. On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, [S. l.], v. 18, n. 1, pp. 50-60, 1947. DOI: https://doi.org/10.1214/aoms/1177730491

OLIVEIRA, Gabriel P.; SILVA, Mariana O.; SEUFITELLI, Danilo B.; LACERDA, Anisio; MORO, Mirella M. Detecting Collaboration Profiles in Success-based Music Genre Networks. In: INTERNATIONAL SOCIETY FOR MUSIC INFORMATION RETRIEVAL CONFERENCE (ISMIR), 2020, Montreal, Canada. Proceedings [...]. [S. l.]: ISMIR, 2020. pp. 726-732.

OLIVEIRA, Gabriel P.; COUTO DA SILVA, Ana Paula; MORO, Mirella M. What makes a viral song? Unraveling music virality factors. In: ACM WEB SCIENCE CONFERENCE (WEBSCI), 2024, Stuttgart, Germany. Proceedings [...]. New York: ACM, 2024. pp. 181-190. DOI: 10.1145/3614419.3644011. DOI: https://doi.org/10.1145/3614419.3644011

PRÓ-MÚSICA BRASIL. Mercado Fonográfico Brasileiro 2022. [S. l.], 2023. Disponível em: <https://pro-musicabr.org.br/wp-content/uploads/2023/03/2023-03-20-Mercado-Brasileiros-em-2023.pdf>. Acesso em: 19 jun. 2024.

RÖDER, Michael; BOTH, Andreas; HINNEBURG, Alexander. Exploring the Space of Topic Coherence Measures. In: ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM), 2015, Shanghai, China. Proceedings [...]. New York: ACM, 2015. pp. 399-408. DOI: 10.1145/2684822.2685324. DOI: https://doi.org/10.1145/2684822.2685324

SEUFITELLI, Danilo B.; OLIVEIRA, Gabriel, P.; SILVA, Mariana O.; BARBOSA, Gabriel R. G.; MELO, Bruna, C.; BOTELHO, Juliana E.; MELO-GOMES, Luiza; MORO, Mirella M. From Compact Discs to Streaming: A Comparison of Eras within the Brazilian Market. Revista Vórtex, [S. l.], v. 10, n. 1, pp. 1-28, 2022. DOI: 10.33871/23179937.2022.10.1.2. DOI: https://doi.org/10.33871/23179937.2022.10.1.2

SEUFITELLI, Danilo B.; OLIVEIRA, Gabriel, P.; SILVA, Mariana O.; MORO, Mirella M. MGD+: An Enhanced Music Genre Dataset with Success-based Networks. In: DATASET SHOWCASE WORKSHOP (DSW), 2023, Belo Horizonte, Brazil. Anais [...]. Porto Alegre: SBC, 2023a. pp. 36-47. DOI: 10.5753/dsw.2023.233826. DOI: https://doi.org/10.5753/dsw.2023.233826

SEUFITELLI, Danilo B.; OLIVEIRA, Gabriel, P.; SILVA, Mariana O.; SCOFIELD, Clarise; MORO, Mirella M. Hit song science: a comprehensive survey and research directions. Journal of New Music Research, [S. l.], v. 52, n. 1, pp. 41-72, 2023b. DOI: 10.1080/09298215.2023.2282999. DOI: https://doi.org/10.1080/09298215.2023.2282999

SHULMAN, Benjamin; SHARMA, Amit; COSLEY, Dan. Predictability of Popularity: Gaps between Prediction and Understanding. In: INTERNATIONAL AAAI CONFERENCE ON WEB AND SOCIAL MEDIA (ICWSM), 10, 2016, Cologne, Germany. Proceedings [...]. [S. l.]: The AAAI Press, 2016. pp. 348-357. DOI: 10.1609/icwsm.v10i1.14748. DOI: https://doi.org/10.1609/icwsm.v10i1.14748

SILVA, Mariana O.; OLIVEIRA, Gabriel P.; SEUFITELLI, Danilo B.; LACERDA, Anisio; MORO, Mirella M. Collaboration as a Driving Factor for Hit Song Classification. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND WEB (WEBMEDIA), 2022, Curitiba, Brazil. Anais [...]. New York: ACM, 2022. pp. 66-74. DOI: 10.1145/3539637.3556993. DOI: https://doi.org/10.1145/3539637.3556993

SILVA, Mariana O.; OLIVEIRA, Gabriel P.; SEUFITELLI, Danilo B.; MORO, Mirella M. Temporal Success Analyses in Music Collaboration Networks: Brazilian and Global Scenarios. Revista Vórtex, [S. l.], v. 11, n. 2, pp. 1-27, 2023. DOI: 10.33871/23179937.2023.11.2.7185. DOI: https://doi.org/10.33871/23179937.2023.11.2.7185

TAUSCZIK, Yla R.; PENNEBAKER, James W. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, [S. l.], v. 29, n. 1, pp. 24-54, 2010. DOI: 10.1177/0261927X09351676. DOI: https://doi.org/10.1177/0261927X09351676

TSIARA, Eleana; TJORTJIS, Christos. Using Twitter to Predict Chart Position for Songs. In: IFIP INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE APPLICATIONS & INNOVATIONS (AIAI), 2020, Neos Marmaras, Greece. Proceedings [...]. New York: Springer, 2020. pp. 62-72. DOI: 10.1007/978-3-030-49161-1_6. DOI: https://doi.org/10.1007/978-3-030-49161-1_6

VAZ DE MELO, Gabriel B.; MACHADO, Ana F.; CARVALHO, Lucas R. de. Music consumption in Brazil: an analysis of streaming reproductions. PragMATIZES - Revista Latino-Americana de Estudos em Cultura, [S. l.], v. 10, n. 19, pp. 141-169, 2020. DOI: 10.22409/pragmatizes.v10i19.40565. DOI: https://doi.org/10.22409/pragmatizes.v10i19.40565

YITZHAKI, Shlomo. Relative Deprivation and the Gini Coefficient. The Quarterly Journal of Economics, [S. l.], v. 93, n. 2, pp. 321-324, 1979. DOI: 10.2307/1883197. DOI: https://doi.org/10.2307/1883197

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Published

2024-09-07

How to Cite

Oliveira, G., Couto da Silva, A. P., & Moro, M. (2024). A quantitative comparison of viral and hit songs in the Brazilian music market. Vortex Music Journal, 12, 1–29. https://doi.org/10.33871/vortex.2024.12.8727

Issue

Vol. 12 (2024): Revista Vórtex

Section

Research Papers

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Copyright (c) 2024 Gabriel P. Oliveira, Ana Paula Couto da Silva, Mirella M. Moro

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