{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T08:30:31Z","timestamp":1721637031870},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,5,31]],"date-time":"2020-05-31T00:00:00Z","timestamp":1590883200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,5,31]],"date-time":"2020-05-31T00:00:00Z","timestamp":1590883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Soc Sc"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s42001-020-00070-z","type":"journal-article","created":{"date-parts":[[2020,5,31]],"date-time":"2020-05-31T12:02:33Z","timestamp":1590926553000},"page":"147-161","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The rhythm of Mexico: an exploratory data analysis of Spotify\u2019s top 50"],"prefix":"10.1007","volume":"4","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-3398-2632","authenticated-orcid":false,"given":"J. Manuel","family":"P\u00e9rez-Verdejo","sequence":"first","affiliation":[]},{"given":"C. A.","family":"Pi\u00f1a-Garc\u00eda","sequence":"additional","affiliation":[]},{"given":"Mario Miguel","family":"Ojeda","sequence":"additional","affiliation":[]},{"given":"A.","family":"Rivera-Lara","sequence":"additional","affiliation":[]},{"given":"L.","family":"M\u00e9ndez-Morales","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,5,31]]},"reference":[{"key":"70_CR1","doi-asserted-by":"publisher","unstructured":"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.","DOI":"10.1371\/journal.pone.0173392"},{"key":"70_CR2","doi-asserted-by":"publisher","unstructured":"Andersen, J. S. (2014). Using the Echo Nest\u2019s automatically extracted music features for a musicological purpose. In 4th International workshop on cognitive information processing\u2014Proceedings of CIP 2014. https:\/\/doi.org\/10.1109\/CIP.2014.6844510. https:\/\/www.ieeexplore.ieee.org\/abstract\/document\/6844510.","DOI":"10.1109\/CIP.2014.6844510"},{"key":"70_CR3","volume-title":"Documenting software architectures: Views and beyond","author":"F Bachmann","year":"2010","unstructured":"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.","edition":"2"},{"key":"70_CR4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0217389","author":"C Bauer","year":"2019","unstructured":"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.","journal-title":"PLoS One"},{"issue":"2","key":"70_CR5","doi-asserted-by":"publisher","first-page":"26:1","DOI":"10.1145\/2652481","volume":"47","author":"G Bonnin","year":"2014","unstructured":"Bonnin, G., & Jannach, D. (2014). Automated generation of music playlists: Survey and experiments. ACM Computing Surveys, 47(2), 26:1\u201326:35. https:\/\/doi.org\/10.1145\/2652481.","journal-title":"ACM Computing Surveys"},{"key":"70_CR6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0170383","author":"M Carlier","year":"2017","unstructured":"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.","journal-title":"PLoS One"},{"key":"70_CR7","unstructured":"Carrillo Valle, \u00c1. (2019). Evoluci\u00f3n del Consumo de Audio en M\u00e9xico. 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."},{"key":"70_CR8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0164783","author":"M Ellamil","year":"2016","unstructured":"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.","journal-title":"PLoS One"},{"key":"70_CR9","doi-asserted-by":"publisher","unstructured":"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.","DOI":"10.5210\/fm.v21i7.6303"},{"key":"70_CR10","doi-asserted-by":"publisher","unstructured":"Febirautami, L. R., Surjandari, I., & Laoh, E. (2019). Determining characteristics of popular local songs in Indonesia\u2019s music market. In Proceedings\u20142018 5th international conference on information science and control engineering, ICISCE 2018 (pp. 197\u2013201). https:\/\/doi.org\/10.1109\/ICISCE.2018.00050. https:\/\/www.ieeexplore.ieee.org\/document\/8612548.","DOI":"10.1109\/ICISCE.2018.00050"},{"key":"70_CR11","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.jrp.2018.06.004","volume":"75","author":"KR Fricke","year":"2018","unstructured":"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\u2013102. https:\/\/doi.org\/10.1016\/j.jrp.2018.06.004.","journal-title":"Journal of Research in Personality"},{"key":"70_CR12","doi-asserted-by":"publisher","unstructured":"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\u201328). https:\/\/doi.org\/10.1109\/MMSP.2013.6659258. https:\/\/www.ieeexplore.ieee.org\/document\/6659258.","DOI":"10.1109\/MMSP.2013.6659258"},{"key":"70_CR13","doi-asserted-by":"publisher","unstructured":"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\/.","DOI":"10.1371\/journal.pone.0144610"},{"key":"70_CR14","doi-asserted-by":"publisher","unstructured":"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.","DOI":"10.1371\/journal.pone.0164029"},{"key":"70_CR15","unstructured":"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."},{"key":"70_CR16","unstructured":"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."},{"key":"70_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-019-09237-4","author":"I Kamehkhosh","year":"2019","unstructured":"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.","journal-title":"User Modeling and User-Adapted Interaction"},{"key":"70_CR18","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.neucom.2017.09.100","volume":"280","author":"I Karydis","year":"2018","unstructured":"Karydis, I., Gkiokas, A., Katsouros, V., & Iliadis, L. (2018). Musical track popularity mining dataset: Extension and experimentation. Neurocomputing, 280, 76\u201385. https:\/\/doi.org\/10.1016\/j.neucom.2017.09.100.","journal-title":"Neurocomputing"},{"key":"70_CR19","doi-asserted-by":"publisher","unstructured":"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\u2013360. https:\/\/doi.org\/10.1038\/s41562-019-0802-4. http:\/\/www.nature.com\/articles\/s41562-019-0802-4.","DOI":"10.1038\/s41562-019-0802-4"},{"key":"70_CR20","unstructured":"Leroi, A. M., & Swire, J. The recovery of the past. The World of Music, 48(3), 43\u201354 (2006). http:\/\/www.jstor.org\/stable\/41699719."},{"issue":"30","key":"70_CR21","doi-asserted-by":"publisher","first-page":"12081","DOI":"10.1073\/pnas.1203182109","volume":"109","author":"RM MacCallum","year":"2012","unstructured":"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\u201312086. https:\/\/doi.org\/10.1073\/pnas.1203182109.","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"5","key":"70_CR22","doi-asserted-by":"publisher","first-page":"150081","DOI":"10.1098\/rsos.150081","volume":"2","author":"M Mauch","year":"2015","unstructured":"Mauch, M., MacCallum, R. M., Levy, M., & Leroi, A. M. (2015). The evolution of popular music: USA 1960\u20132010. Royal Society Open Science, 2(5), 150081. https:\/\/doi.org\/10.1098\/rsos.150081.","journal-title":"Royal Society Open Science"},{"key":"70_CR23","unstructured":"Middlebrook, K., & Sheik, K. (2019). Song hit prediction: Predicting billboard hits using Spotify data (pp. 1\u20136). arxiv:1908.08609."},{"key":"70_CR24","doi-asserted-by":"publisher","unstructured":"Pichl, M., Zangerle, E., & Specht, G. (2017). Understanding playlist creation on music streaming platforms. In Proceedings\u20142016 IEEE international symposium on multimedia, ISM 2016 (pp. 475\u2013480). IEEE. https:\/\/doi.org\/10.1109\/ISM.2016.139. http:\/\/www.ieeexplore.ieee.org\/document\/7823674\/.","DOI":"10.1109\/ISM.2016.139"},{"issue":"1","key":"70_CR25","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s42001-017-0002-9","volume":"1","author":"CA Pi\u00f1a-Garc\u00eda","year":"2018","unstructured":"Pi\u00f1a-Garc\u00eda, C. A., Siqueiros-Garc\u00eda, J. M., Robles-Belmont, E., Carre\u00f3n, G., Gershenson, C., & L\u00f3pez, J. A. D. (2018). From neuroscience to computer science: a topical approach on twitter. Journal of Computational Social Science, 1(1), 187\u2013208. https:\/\/doi.org\/10.1007\/s42001-017-0002-9.","journal-title":"Journal of Computational Social Science"},{"key":"70_CR26","doi-asserted-by":"publisher","DOI":"10.1145\/3293688.3293696","author":"S Sangnark","year":"2018","unstructured":"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.","journal-title":"ACM International Conference Proceeding Series"},{"issue":"1145\/3132498","key":"70_CR27","first-page":"3133836","volume":"10","author":"VJ Schettino","year":"2017","unstructured":"Schettino, V. J., David, J. M. N., Braga, R., & Ara\u00fajo, M. A. P. (2017). Spotify characterization as a sofware ecosystem. ACM International Conference Proceeding Series Part, 10(1145\/3132498), 3133836.","journal-title":"ACM International Conference Proceeding Series Part"},{"key":"70_CR28","doi-asserted-by":"publisher","unstructured":"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\u201919 (pp. 25\u201330). New York, NY, USA: ACM. https:\/\/doi.org\/10.1145\/3349611.3355546.","DOI":"10.1145\/3349611.3355546"},{"key":"70_CR29","unstructured":"Skid\u00e9n, P. (2016). API improvements and U | Spotify for developers. https:\/\/www.developer.spotify.com\/community\/news\/2016\/03\/29\/api-improvements-update\/."},{"key":"70_CR30","unstructured":"Spotify. (2019). Get Audio Features for a Track | Spotify for Developers. https:\/\/www.developer.spotify.com\/documentation\/web-api\/reference\/tracks\/get-audio-features\/."},{"key":"70_CR31","doi-asserted-by":"publisher","unstructured":"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\u20133142). https:\/\/doi.org\/10.1109\/BigData.2017.8258290.","DOI":"10.1109\/BigData.2017.8258290"},{"key":"70_CR32","doi-asserted-by":"publisher","unstructured":"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.","DOI":"10.1371\/journal.pone.0110490"},{"key":"70_CR33","unstructured":"TheEchoNest. (2015). The Echo Nest. http:\/\/www.the.echonest.com\/."},{"key":"70_CR34","doi-asserted-by":"publisher","DOI":"10.1145\/3281746","author":"Y Yu","year":"2019","unstructured":"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.","journal-title":"ACM Transactions on Multimedia Computing, Communications and Applications."}],"container-title":["Journal of Computational Social Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42001-020-00070-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42001-020-00070-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42001-020-00070-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T23:29:16Z","timestamp":1622417356000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42001-020-00070-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,31]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["70"],"URL":"https:\/\/doi.org\/10.1007\/s42001-020-00070-z","relation":{},"ISSN":["2432-2717","2432-2725"],"issn-type":[{"value":"2432-2717","type":"print"},{"value":"2432-2725","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,31]]},"assertion":[{"value":"5 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}