A quantitative comparison of viral and hit songs in the Brazilian music market
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https://doi.org/10.33871/vortex.2024.12.8727Palabras clave:
song virality, musical success, quantitative analysis, BrazilResumen
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.
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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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Derechos de autor 2024 Gabriel P. Oliveira, Ana Paula Couto da Silva, Mirella M. Moro
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