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The rhythm of Mexico: an exploratory data analysis of Spotify’s top 50

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Abstract

Spotify has emerged as an important online platform for streaming digital music. A key aspect of Spotify is that it provides access to music on-demand to a worldwide level. In this regard, Spotify via its API permits to gain access to music-related data with the aim to know information about different parameters such as: artist, album, and genre. This paper aims to: (1) give an overview of the shared features of the songs that appeared at Mexico’s top 50 during 2019, (2) analyze how these features are related to a track permanence on the top 50; and (3) compare those results with the global top 50 chart.

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Notes

  1. https://www.spotify.com.

  2. https://www.apple.com/apple-music/.

  3. https://www.play.google.com/music.

  4. https://www.developer.spotify.com/documentation/web-api/.

References

  1. Aljanaki, A., Yang, Y. H., & Soleymani, M. (2017). Developing a benchmark for emotional analysis of music. PLoS One 12(3). https://doi.org/10.1371/journal.pone.0173392. http://www.mturk.com.

  2. Andersen, J. S. (2014). Using the Echo Nest’s automatically extracted music features for a musicological purpose. In 4th International workshop on cognitive information processing—Proceedings of CIP 2014. https://doi.org/10.1109/CIP.2014.6844510. https://www.ieeexplore.ieee.org/abstract/document/6844510.

  3. Bachmann, F., Bass, L., Clements, P., Garlan, D., Ivers, J., Little, M., et al. (2010). Documenting software architectures: Views and beyond (2nd ed.). Boston: Addison-Wesley Professional.

    Google Scholar 

  4. Bauer, C., & Schedl, M. (2019). Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems. PLoS One,. https://doi.org/10.1371/journal.pone.0217389.

    Article  Google Scholar 

  5. Bonnin, G., & Jannach, D. (2014). Automated generation of music playlists: Survey and experiments. ACM Computing Surveys, 47(2), 26:1–26:35. https://doi.org/10.1145/2652481.

    Article  Google Scholar 

  6. Carlier, M., & Delevoye-Turrell, Y. (2017). Tolerance to exercise intensity modulates pleasure when exercising in music: The upsides of acoustic energy for high tolerant individuals. PLoS One,. https://doi.org/10.1371/journal.pone.0170383.

    Article  Google Scholar 

  7. Carrillo Valle, Á. (2019). Evolución del Consumo de Audio en México. Technical report, The Competitive Intelligence Unit. https://www.theciu.com/publicaciones-2/2019/1/26/evolucin-del-consumo-de-audio-ott-en-mxico. Accessed 28 May 2020.

  8. Ellamil, M., Berson, J., Wong, J., Buckley, L., & Margulies, D. S. (2016). One in the dance: Musical correlates of group synchrony in a real-world club environment. PLoS One,. https://doi.org/10.1371/journal.pone.0164783.

    Article  Google Scholar 

  9. Eriksson, M. (2016). Close reading big data: The Echo Nest and the production of (rotten) music metadata by Maria Eriksson. First Monday, 21(7). https://doi.org/10.5210/fm.v21i7.6303. https://www.journals.uic.edu/ojs/index.php/fm/article/view/6303/5530.

  10. Febirautami, L. R., Surjandari, I., & Laoh, E. (2019). Determining characteristics of popular local songs in Indonesia’s music market. In Proceedings—2018 5th international conference on information science and control engineering, ICISCE 2018 (pp. 197–201). https://doi.org/10.1109/ICISCE.2018.00050. https://www.ieeexplore.ieee.org/document/8612548.

  11. Fricke, K. R., Greenberg, D. M., Rentfrow, P. J., & Herzberg, P. Y. (2018). Computer-based music feature analysis mirrors human perception and can be used to measure individual music preference. Journal of Research in Personality, 75, 94–102. https://doi.org/10.1016/j.jrp.2018.06.004.

    Article  Google Scholar 

  12. Germain, A., & Chakareski, J. (2013). Spotify me: Facebook-assisted automatic playlist generation. In 2013 IEEE international workshop on multimedia signal processing, MMSP 2013 (pp. 25–28). https://doi.org/10.1109/MMSP.2013.6659258. https://www.ieeexplore.ieee.org/document/6659258.

  13. Giannakopoulos, T. (2015). PyAudioAnalysis: An open-source python library for audio signal analysis. PLoS One, 10(12). https://doi.org/10.1371/journal.pone.0144610. https://www.github.com/tyiannak/pyAudioAnalysis/.

  14. Hall, S. E., Schubert, E., & Wilson, S. J. (2016). The role of trait and state absorption in the enjoyment of music. PLoS One, 11(11) (2016). https://doi.org/10.1371/journal.pone.0164029. http://www.arc.gov.

  15. Hern, A. Spotify acquires music data firm The Echo Nest | Technology | The Guardian. https://www.theguardian.com/technology/2014/mar/06/spotify-echo-nest-streaming-music-deal. Accessed 28 May 2020.

  16. IFPI. (2019). Music Listening 2019. Technical report, International Federation of the Phonographic Industry. https://www.ifpi.org/downloads/Music-Listening-2019.pdf. Accessed 28 May 2020.

  17. Kamehkhosh, I., Bonnin, G., & Jannach, D. (2019). Effects of recommendations on the playlist creation behavior of users. User Modeling and User-Adapted Interaction,. https://doi.org/10.1007/s11257-019-09237-4.

    Article  Google Scholar 

  18. Karydis, I., Gkiokas, A., Katsouros, V., & Iliadis, L. (2018). Musical track popularity mining dataset: Extension and experimentation. Neurocomputing, 280, 76–85. https://doi.org/10.1016/j.neucom.2017.09.100.

    Article  Google Scholar 

  19. Lambert, B., Kontonatsios, G., Mauch, M., Kokkoris, T., Jockers, M., Ananiadou, S., et al. (2020). The pace of modern culture. Nature Human Behaviour, 4(4), 352–360. https://doi.org/10.1038/s41562-019-0802-4. http://www.nature.com/articles/s41562-019-0802-4.

  20. Leroi, A. M., & Swire, J. The recovery of the past. The World of Music, 48(3), 43–54 (2006). http://www.jstor.org/stable/41699719.

  21. MacCallum, R. M., Mauch, M., Burt, A., & Leroi, A. M. (2012). Evolution of music by public choice. Proceedings of the National Academy of Sciences, 109(30), 12081–12086. https://doi.org/10.1073/pnas.1203182109.

    Article  Google Scholar 

  22. Mauch, M., MacCallum, R. M., Levy, M., & Leroi, A. M. (2015). The evolution of popular music: USA 1960–2010. Royal Society Open Science, 2(5), 150081. https://doi.org/10.1098/rsos.150081.

    Article  Google Scholar 

  23. Middlebrook, K., & Sheik, K. (2019). Song hit prediction: Predicting billboard hits using Spotify data (pp. 1–6). arxiv:1908.08609.

  24. Pichl, M., Zangerle, E., & Specht, G. (2017). Understanding playlist creation on music streaming platforms. In Proceedings—2016 IEEE international symposium on multimedia, ISM 2016 (pp. 475–480). IEEE. https://doi.org/10.1109/ISM.2016.139. http://www.ieeexplore.ieee.org/document/7823674/.

  25. Piña-García, C. A., Siqueiros-García, J. M., Robles-Belmont, E., Carreón, G., Gershenson, C., & López, J. A. D. (2018). From neuroscience to computer science: a topical approach on twitter. Journal of Computational Social Science, 1(1), 187–208. https://doi.org/10.1007/s42001-017-0002-9.

    Article  Google Scholar 

  26. Sangnark, S., Lertwatechakul, M., & Benjangkaprasert, C. (2018). Thai music emotion recognition by linear regression. ACM International Conference Proceeding Series,. https://doi.org/10.1145/3293688.3293696.

    Article  Google Scholar 

  27. Schettino, V. J., David, J. M. N., Braga, R., & Araújo, M. A. P. (2017). Spotify characterization as a sofware ecosystem. ACM International Conference Proceeding Series Part, 10(1145/3132498), 3133836.

    Google Scholar 

  28. Schwind, A., Haberzettl, L., Wamser, F., & Ho\(\backslash\)ssfeld, T. (2019). QoE analysis of spotify audio streaming and app browsing. In Proceedings of the 4th Internet-QoE workshop on QoE-based analysis and management of data communication networks, Internet-QoE’19 (pp. 25–30). New York, NY, USA: ACM. https://doi.org/10.1145/3349611.3355546.

  29. Skidén, P. (2016). API improvements and U | Spotify for developers. https://www.developer.spotify.com/community/news/2016/03/29/api-improvements-update/.

  30. Spotify. (2019). Get Audio Features for a Track | Spotify for Developers. https://www.developer.spotify.com/documentation/web-api/reference/tracks/get-audio-features/.

  31. Takano, M., Mizukami, H., Toriumi, F., Takeuchi, M., Wada, K., Yasuda, M., & Fukiida, I. (2017). Analysis of the changes in listening trends of a music streaming service. In: 2017 IEEE international conference on big data (big data) (pp. 3139–3142). https://doi.org/10.1109/BigData.2017.8258290.

  32. Taruffi, L., & Koelsch, S. (2014). The paradox of music-evoked sadness: An online survey. PLoS One, 9(10), 110490. https://doi.org/10.1371/journal.pone.0110490. http://www.plosone.org.

  33. TheEchoNest. (2015). The Echo Nest. http://www.the.echonest.com/.

  34. Yu, Y., Tang, S., Raposo, F., & Chen, L. (2019). Deep cross-modal correlation learning for audio and lyrics in music retrieval. ACM Transactions on Multimedia Computing, Communications and Applications.,. https://doi.org/10.1145/3281746.

    Article  Google Scholar 

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Authors and Affiliations

  1. Facultad de Estadística e Informática, Universidad Veracruzana, Xalapa, Veracruz, Mexico

    J. Manuel Pérez-Verdejo, Mario Miguel Ojeda, A. Rivera-Lara & L. Méndez-Morales

  2. Centro de Estudios de Opinión y Análisis, Laboratorio para el Análisis de Informacián Generada a través de las Redes Sociales en Internet (LARSI), Universidad Veracruzana, Xalapa, Veracruz, Mexico

    J. Manuel Pérez-Verdejo & C. A. Piña-García

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  1. J. Manuel Pérez-Verdejo
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Correspondence to J. Manuel Pérez-Verdejo.

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Pérez-Verdejo, J.M., Piña-García, C.A., Ojeda, M.M. et al. The rhythm of Mexico: an exploratory data analysis of Spotify’s top 50. J Comput Soc Sc 4, 147–161 (2021). https://doi.org/10.1007/s42001-020-00070-z

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