{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:13:57Z","timestamp":1753600437363,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:00:00Z","timestamp":1667779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Minas Gerais","award":["APQ-02701-21"],"award-info":[{"award-number":["APQ-02701-21"]}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["427531\/2018-7"],"award-info":[{"award-number":["427531\/2018-7"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,11,7]]},"DOI":"10.1145\/3539637.3556993","type":"proceedings-article","created":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T22:14:00Z","timestamp":1664230440000},"page":"66-74","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Collaboration as a Driving Factor for Hit Song Classification"],"prefix":"10.1145","author":[{"given":"Mariana O.","family":"Silva","sequence":"first","affiliation":[{"name":"Departamento de Ci\u00eancia da Computa\u00e7\u00e3o, Universidade Federal de Minas Gerais (UFMG), Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriel P.","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Departamento de Ci\u00eancia da Computa\u00e7\u00e3o, Universidade Federal de Minas Gerais (UFMG), Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danilo B.","family":"Seufitelli","sequence":"additional","affiliation":[{"name":"Departamento de Ci\u00eancia da Computa\u00e7\u00e3o, Universidade Federal de Minas Gerais (UFMG), Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anisio","family":"Lacerda","sequence":"additional","affiliation":[{"name":"Departamento de Ci\u00eancia da Computa\u00e7\u00e3o, Universidade Federal de Minas Gerais (UFMG), Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mirella M.","family":"Moro","sequence":"additional","affiliation":[{"name":"Departamento de Ci\u00eancia da Computa\u00e7\u00e3o, Universidade Federal de Minas Gerais (UFMG), Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"key":"#cr-split#-e_1_3_2_1_1_1.1","doi-asserted-by":"crossref","unstructured":"Carlos Almada 2019. J-Analyzer: A Software for Computer-Assisted Analysis of Ant\u00f4nio Carlos Jobims Songs. In SBCM. SBC Brazil 12-16. https:\/\/doi.org\/10.5753\/sbcm.2019.10416 10.5753\/sbcm.2019.10416","DOI":"10.5753\/sbcm.2019.10416"},{"key":"#cr-split#-e_1_3_2_1_1_1.2","doi-asserted-by":"crossref","unstructured":"Carlos Almada 2019. J-Analyzer: A Software for Computer-Assisted Analysis of Ant\u00f4nio Carlos Jobims Songs. In SBCM. SBC Brazil 12-16. https:\/\/doi.org\/10.5753\/sbcm.2019.10416","DOI":"10.5753\/sbcm.2019.10416"},{"key":"#cr-split#-e_1_3_2_1_2_1.1","doi-asserted-by":"crossref","unstructured":"Carlos\u00a0V.S. Araujo 2017. Predicting Music Success Based on Users' Comments on Online Social Networks. In WebMedia. SBC Brazil 149-156. https:\/\/doi.org\/10.1145\/3126858.3126885 10.1145\/3126858.3126885","DOI":"10.1145\/3126858.3126885"},{"key":"#cr-split#-e_1_3_2_1_2_1.2","doi-asserted-by":"crossref","unstructured":"Carlos\u00a0V.S. Araujo 2017. Predicting Music Success Based on Users' Comments on Online Social Networks. In WebMedia. SBC Brazil 149-156. https:\/\/doi.org\/10.1145\/3126858.3126885","DOI":"10.1145\/3126858.3126885"},{"key":"e_1_3_2_1_3_1","volume-title":"Predicting Music Popularity Using Music Charts","author":"Araujo V.S.","year":"2019","unstructured":"Carlos\u00a0 V.S. Araujo , Marco A.\u00a0P. de Cristo , and Rafael Giusti . 2019. Predicting Music Popularity Using Music Charts . In ICMLA. IEEE, Boca Raton , Florida, USA , 859\u2013864. https:\/\/doi.org\/10.1109\/ICMLA. 2019 .00149 10.1109\/ICMLA.2019.00149 Carlos\u00a0V.S. Araujo, Marco A.\u00a0P. de Cristo, and Rafael Giusti. 2019. Predicting Music Popularity Using Music Charts. In ICMLA. IEEE, Boca Raton, Florida, USA, 859\u2013864. https:\/\/doi.org\/10.1109\/ICMLA.2019.00149"},{"volume-title":"Advanced Data Mining and Applications","author":"Kerstin Bischoff","key":"e_1_3_2_1_4_1","unstructured":"Kerstin Bischoff 2009. Social Knowledge-Driven Music Hit Prediction . In Advanced Data Mining and Applications . Springer , Berlin, Heidelberg , 43\u201354. https:\/\/doi.org\/10.1007\/978-3-642-03348-3_8 10.1007\/978-3-642-03348-3_8 Kerstin Bischoff 2009. Social Knowledge-Driven Music Hit Prediction. In Advanced Data Mining and Applications. Springer, Berlin, Heidelberg, 43\u201354. https:\/\/doi.org\/10.1007\/978-3-642-03348-3_8"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3148330.3148346"},{"key":"#cr-split#-e_1_3_2_1_6_1.1","doi-asserted-by":"crossref","unstructured":"Alberto Cosimato 2019. The Conundrum of Success in Music: Playing it or Talking About it?IEEE Access 7(2019) 123289-123298. https:\/\/doi.org\/10.1109\/ACCESS.2019.2937743 10.1109\/ACCESS.2019.2937743","DOI":"10.1109\/ACCESS.2019.2937743"},{"key":"#cr-split#-e_1_3_2_1_6_1.2","doi-asserted-by":"crossref","unstructured":"Alberto Cosimato 2019. The Conundrum of Success in Music: Playing it or Talking About it?IEEE Access 7(2019) 123289-123298. https:\/\/doi.org\/10.1109\/ACCESS.2019.2937743","DOI":"10.1109\/ACCESS.2019.2937743"},{"key":"#cr-split#-e_1_3_2_1_7_1.1","doi-asserted-by":"crossref","unstructured":"Angelo C.\u00a0M. da Silva Diego\u00a0F. Silva and Ricardo\u00a0M. Marcacini. 2020. 4MuLA: A Multitask Multimodal and Multilingual Dataset of Music Lyrics and Audio Features. In WebMedia. ACM Brazil 145-148. https:\/\/doi.org\/10.1145\/3428658.3431089 10.1145\/3428658.3431089","DOI":"10.1145\/3428658.3431089"},{"key":"#cr-split#-e_1_3_2_1_7_1.2","doi-asserted-by":"crossref","unstructured":"Angelo C.\u00a0M. da Silva Diego\u00a0F. Silva and Ricardo\u00a0M. Marcacini. 2020. 4MuLA: A Multitask Multimodal and Multilingual Dataset of Music Lyrics and Audio Features. In WebMedia. ACM Brazil 145-148. https:\/\/doi.org\/10.1145\/3428658.3431089","DOI":"10.1145\/3428658.3431089"},{"key":"#cr-split#-e_1_3_2_1_8_1.1","doi-asserted-by":"crossref","unstructured":"Marcos\u00a0A. de Almeida 2017. The Fast and Winding Roads that Lead to The Doors: Generating Heterogeneous Music Playlists. In WebMedia. ACM Brazil 269-276. https:\/\/doi.org\/10.1145\/3126858.3126891 10.1145\/3126858.3126891","DOI":"10.1145\/3126858.3126891"},{"key":"#cr-split#-e_1_3_2_1_8_1.2","doi-asserted-by":"crossref","unstructured":"Marcos\u00a0A. de Almeida 2017. The Fast and Winding Roads that Lead to The Doors: Generating Heterogeneous Music Playlists. In WebMedia. ACM Brazil 269-276. https:\/\/doi.org\/10.1145\/3126858.3126891","DOI":"10.1145\/3126858.3126891"},{"volume-title":"Automatic Prediction of Hit Songs","author":"Dhanaraj Ruth","key":"e_1_3_2_1_9_1","unstructured":"Ruth Dhanaraj and Beth Logan . 2005. Automatic Prediction of Hit Songs . In ISMIR. Int\u2019l Society for Music Information Retrieval , London, UK , 488\u2013491. Ruth Dhanaraj and Beth Logan. 2005. Automatic Prediction of Hit Songs. In ISMIR. Int\u2019l Society for Music Information Retrieval, London, UK, 488\u2013491."},{"key":"e_1_3_2_1_10_1","unstructured":"Aur\u00e9lien G\u00e9ron. 2019. Hands-on machine learning with Scikit-Learn Keras and TensorFlow: Concepts tools and techniques to build intelligent systems. O\u2019Reilly Media USA.  Aur\u00e9lien G\u00e9ron. 2019. Hands-on machine learning with Scikit-Learn Keras and TensorFlow: Concepts tools and techniques to build intelligent systems. O\u2019Reilly Media USA."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.171274"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2021.113535"},{"key":"#cr-split#-e_1_3_2_1_13_1.1","doi-asserted-by":"crossref","unstructured":"Yekyung Kim Bongwon Suh and Kyogu Lee. 2014. # nowplaying the future Billboard: mining music listening behaviors of Twitter users for hit song prediction. In SoMeRA. ACM Gold Coast Australia 51-56. https:\/\/doi.org\/10.1145\/2632188.2632206 10.1145\/2632188.2632206","DOI":"10.1145\/2632188.2632206"},{"key":"#cr-split#-e_1_3_2_1_13_1.2","doi-asserted-by":"crossref","unstructured":"Yekyung Kim Bongwon Suh and Kyogu Lee. 2014. # nowplaying the future Billboard: mining music listening behaviors of Twitter users for hit song prediction. In SoMeRA. ACM Gold Coast Australia 51-56. https:\/\/doi.org\/10.1145\/2632188.2632206","DOI":"10.1145\/2632188.2632206"},{"key":"e_1_3_2_1_14_1","volume-title":"Lundberg and Su-In Lee","author":"M.","year":"2017","unstructured":"Scott\u00a0 M. Lundberg and Su-In Lee . 2017 . A Unified Approach to Interpreting Model Predictions. In NIPS. Curran Associates Inc., Long Beach, California, USA , 4768\u20134777. Scott\u00a0M. Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In NIPS. Curran Associates Inc., Long Beach, California, USA, 4768\u20134777."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2976033"},{"volume-title":"Machine learning - a probabilistic perspective","author":"Murphy P.","key":"e_1_3_2_1_16_1","unstructured":"Kevin\u00a0 P. Murphy . 2012. Machine learning - a probabilistic perspective . MIT Press , Cambridge, USA. Kevin\u00a0P. Murphy. 2012. Machine learning - a probabilistic perspective. MIT Press, Cambridge, USA."},{"key":"e_1_3_2_1_17_1","volume-title":"Intl. Workshop on Mach. Learn. and Music. NIPS","author":"Ni Yizhao","year":"2011","unstructured":"Yizhao Ni 2011 . Hit song science once again a science? . In Intl. Workshop on Mach. Learn. and Music. NIPS , Sierra Nevada, Spain. Yizhao Ni 2011. Hit song science once again a science?. In Intl. Workshop on Mach. Learn. and Music. NIPS, Sierra Nevada, Spain."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1177\/1029864914548528"},{"volume-title":"Music Data Mining","author":"Pachet Fran\u00e7ois","key":"e_1_3_2_1_19_1","unstructured":"Fran\u00e7ois Pachet . 2011. Hit song science . In Music Data Mining , Tao Li, Mitsunori Ogihara, and George Tzanetakis (Eds.). CRC Press , USA , Chapter\u00a010, 305\u2013326. Fran\u00e7ois Pachet. 2011. Hit song science. In Music Data Mining, Tao Li, Mitsunori Ogihara, and George Tzanetakis (Eds.). CRC Press, USA, Chapter\u00a010, 305\u2013326."},{"volume-title":"Hit Song Science Is Not Yet a Science","author":"Pachet Fran\u00e7ois","key":"e_1_3_2_1_20_1","unstructured":"Fran\u00e7ois Pachet and Pierre Roy . 2008. Hit Song Science Is Not Yet a Science . In ISMIR. Int\u2019l Society for Music Information Retrieval , Philadelphia, USA , 355\u2013360. Fran\u00e7ois Pachet and Pierre Roy. 2008. Hit Song Science Is Not Yet a Science. In ISMIR. Int\u2019l Society for Music Information Retrieval, Philadelphia, USA, 355\u2013360."},{"key":"#cr-split#-e_1_3_2_1_21_1.1","doi-asserted-by":"crossref","unstructured":"Jing Ren Jialie Shen and Robert\u00a0J. Kauffman. 2016. What Makes a Music Track Popular in Online Social Networks?. In WWW. ACM Montreal Canada 95-96. https:\/\/doi.org\/10.1145\/2872518.2889402 10.1145\/2872518.2889402","DOI":"10.1145\/2872518.2889402"},{"key":"#cr-split#-e_1_3_2_1_21_1.2","doi-asserted-by":"crossref","unstructured":"Jing Ren Jialie Shen and Robert\u00a0J. Kauffman. 2016. What Makes a Music Track Popular in Online Social Networks?. In WWW. ACM Montreal Canada 95-96. https:\/\/doi.org\/10.1145\/2872518.2889402","DOI":"10.1145\/2872518.2889402"},{"volume-title":"The Computer Music Tutorial","author":"Roads Curtis","key":"e_1_3_2_1_22_1","unstructured":"Curtis Roads . 1996. The Computer Music Tutorial . MIT Press , Cambridge, England . Curtis Roads. 1996. The Computer Music Tutorial. MIT Press, Cambridge, England."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1017\/S1355771805000919"},{"key":"#cr-split#-e_1_3_2_1_24_1.1","doi-asserted-by":"crossref","unstructured":"Arthur\u00a0C. Serra 2021. Quality Enhancement of Highly Degraded Music Using Deep Learning-Based Prediction Models for Lost Frequencies. In WebMedia. ACM Brazil 205-211. https:\/\/doi.org\/10.1145\/3470482.3479635 10.1145\/3470482.3479635","DOI":"10.1145\/3470482.3479635"},{"key":"#cr-split#-e_1_3_2_1_24_1.2","doi-asserted-by":"crossref","unstructured":"Arthur\u00a0C. Serra 2021. Quality Enhancement of Highly Degraded Music Using Deep Learning-Based Prediction Models for Lost Frequencies. In WebMedia. ACM Brazil 205-211. https:\/\/doi.org\/10.1145\/3470482.3479635","DOI":"10.1145\/3470482.3479635"},{"key":"e_1_3_2_1_25_1","volume-title":"Silva and Mirella\u00a0M Moro","author":"O.","year":"2019","unstructured":"Mariana\u00a0 O. Silva and Mirella\u00a0M Moro . 2019 . Causality Analysis Between Collaboration Profiles and Musical Success. In WebMedia. ACM, Rio de Janeiro , 369\u2013376. https:\/\/doi.org\/10.1145\/3323503.3349549 10.1145\/3323503.3349549 Mariana\u00a0O. Silva and Mirella\u00a0M Moro. 2019. Causality Analysis Between Collaboration Profiles and Musical Success. In WebMedia. ACM, Rio de Janeiro, 369\u2013376. https:\/\/doi.org\/10.1145\/3323503.3349549"},{"key":"#cr-split#-e_1_3_2_1_26_1.1","unstructured":"Mariana\u00a0O. Silva La\u00eds Mota and Mirella\u00a0M. Moro. 2019. MusicOSet: An Enhanced Open Dataset for Music Data Mining. https:\/\/doi.org\/10.5281\/zenodo.4904639 https:\/\/doi.org\/10.5281\/zenodo.4904639. 10.5281\/zenodo.4904639"},{"key":"#cr-split#-e_1_3_2_1_26_1.2","unstructured":"Mariana\u00a0O. Silva La\u00eds Mota and Mirella\u00a0M. Moro. 2019. MusicOSet: An Enhanced Open Dataset for Music Data Mining. https:\/\/doi.org\/10.5281\/zenodo.4904639 https:\/\/doi.org\/10.5281\/zenodo.4904639."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3297280.3297483"},{"key":"e_1_3_2_1_28_1","volume-title":"MusicOSet: An Enhanced Open Dataset for Music Data Mining. In XXXII Simp\u00f3sio Brasileiro de Banco de Dados: Dataset Showcase Workshop, SBBD 2019 Companion. SBC, Fortaleza, CE, Brazil, 8\u201317","author":"Silva O.","year":"2019","unstructured":"Mariana\u00a0 O. Silva , La\u00eds\u00a0Mota de Alencar Rocha , and Mirella\u00a0 M. Moro . 2019 . MusicOSet: An Enhanced Open Dataset for Music Data Mining. In XXXII Simp\u00f3sio Brasileiro de Banco de Dados: Dataset Showcase Workshop, SBBD 2019 Companion. SBC, Fortaleza, CE, Brazil, 8\u201317 . Mariana\u00a0O. Silva, La\u00eds\u00a0Mota de Alencar Rocha, and Mirella\u00a0M. Moro. 2019. MusicOSet: An Enhanced Open Dataset for Music Data Mining. In XXXII Simp\u00f3sio Brasileiro de Banco de Dados: Dataset Showcase Workshop, SBBD 2019 Companion. SBC, Fortaleza, CE, Brazil, 8\u201317."},{"key":"e_1_3_2_1_29_1","volume-title":"Novel Datasets for Evaluating Song Popularity Prediction Tasks. In IEEE International Symposium on Multimedia (ISM). IEEE","author":"V\u00f6tter Michael","year":"2021","unstructured":"Michael V\u00f6tter 2021 . Novel Datasets for Evaluating Song Popularity Prediction Tasks. In IEEE International Symposium on Multimedia (ISM). IEEE , Los Alamitos, USA, 166\u2013173. https:\/\/doi.org\/10.1109\/ISM52913. 2021.00034 10.1109\/ISM52913.2021.00034 Michael V\u00f6tter 2021. Novel Datasets for Evaluating Song Popularity Prediction Tasks. In IEEE International Symposium on Multimedia (ISM). IEEE, Los Alamitos, USA, 166\u2013173. https:\/\/doi.org\/10.1109\/ISM52913.2021.00034"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7952230"},{"volume-title":"Hit Song Prediction: Leveraging Low- and High-Level Audio Features","author":"Zangerle Eva","key":"e_1_3_2_1_31_1","unstructured":"Eva Zangerle , Michael V\u00f6tter , Ramona Huber , and Yi-Hsuan Yang . 2019. Hit Song Prediction: Leveraging Low- and High-Level Audio Features . In ISMIR. Int\u2019l Society for Music Information Retrieval , Delft, Netherlands , 319\u2013326. Eva Zangerle, Michael V\u00f6tter, Ramona Huber, and Yi-Hsuan Yang. 2019. Hit Song Prediction: Leveraging Low- and High-Level Audio Features. In ISMIR. Int\u2019l Society for Music Information Retrieval, Delft, Netherlands, 319\u2013326."}],"event":{"name":"WebMedia '22: Brazilian Symposium on Multimedia and Web","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGMM ACM Special Interest Group on Multimedia"],"location":"Curitiba Brazil","acronym":"WebMedia '22"},"container-title":["Proceedings of the Brazilian Symposium on Multimedia and the Web"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539637.3556993","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3539637.3556993","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:38:03Z","timestamp":1750178283000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539637.3556993"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,7]]},"references-count":40,"alternative-id":["10.1145\/3539637.3556993","10.1145\/3539637"],"URL":"https:\/\/doi.org\/10.1145\/3539637.3556993","relation":{},"subject":[],"published":{"date-parts":[[2022,11,7]]},"assertion":[{"value":"2022-11-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}