A quantitative comparison of viral and hit songs in the Brazilian music market
Visualizações: 52DOI:
https://doi.org/10.33871/vortex.2024.12.8727Palavras-chave:
song virality, musical success, quantitative analysis, BrazilResumo
A viralização de músicas através de plataformas de streaming e redes sociais é comum, mas nem todas as músicas virais se tornam sucessos. Neste contexto, nosso objetivo é descobrir o que difere as músicas virais dos hits para além da definição. Nós utilizamos uma metodologia quantitativa em paradas de sucesso do mercado brasileiro. Comparamos músicas de sucesso e virais quanto às suas características intrínsecas e extrínsecas, e os resultados revelam diferenças significativas entre elas. Características como gêneros musicais, tópicos das letras e emoções surgem como elementos cruciais para distinguir tais canções no contexto brasileiro. Além disso, características temporais indicam diferenças nos processos de difusão entre hits e virais. Em geral, este estudo oferece percepções sobre o consumo de música no Brasil, revelando a conexão entre as características das músicas e seu sucesso e viralização em plataformas de streaming.
Downloads
Referências
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
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2024 Gabriel P. Oliveira, Ana Paula Couto da Silva, Mirella M. Moro
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Autores mantêm os direitos autorais e concedem à revista o direito de primeira publicação, com o trabalho simultaneamente licenciado sob a Licença Creative Commons Attribution que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista.