Article

Social Knowledge-Driven Music Hit Prediction

Authors:

Raluca PaiuAuthors Info & Claims

ADMA '09: Proceedings of the 5th International Conference on Advanced Data Mining and Applications

Pages 43 - 54

https://doi.org/10.1007/978-3-642-03348-3_8

Published: 14 August 2009 Publication History

Abstract

What makes a song to a chart hit? Many people are trying to find the answer to this question. Previous attempts to identify hit songs have mostly focused on the intrinsic characteristics of the songs, such as lyrics and audio features. As social networks become more and more popular and some specialize on certain topics, information about users' music tastes becomes available and easy to exploit. In the present paper we introduce a new method for predicting the potential of music tracks for becoming hits, which instead of relying on intrinsic characteristics of the tracks directly uses data mined from a music social network and the relationships between tracks, artists and albums. We evaluate the performance of our algorithms through a set of experiments and the results indicate good accuracy in correctly identifying music hits, as well as significant improvement over existing approaches.

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Cited By

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Oliveira GCouto da Silva AMoro M(2024)What makes a viral song? Unraveling music virality factorsProceedings of the 16th ACM Web Science Conference10.1145/3614419.3644011(181-190)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3614419.3644011
Silva MOliveira GSeufitelli DLacerda AMoro M(2022)Collaboration as a Driving Factor for Hit Song ClassificationProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3539637.3556993(66-74)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3539637.3556993
Silva MMoro Mdos Santos JMuchaluat Saade Dda Graça C. Pimentel MMacedo A(2019)Causality analysis between collaboration profiles and musical successProceedings of the 25th Brazillian Symposium on Multimedia and the Web10.1145/3323503.3349549(369-376)Online publication date: 29-Oct-2019
https://dl.acm.org/doi/10.1145/3323503.3349549
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Index Terms

Social Knowledge-Driven Music Hit Prediction
1. Applied computing
  1. Arts and humanities
    1. Sound and music computing

Index terms have been assigned to the content through auto-classification.

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Published In

ADMA '09: Proceedings of the 5th International Conference on Advanced Data Mining and Applications

August 2009

803 pages

ISBN:9783642033476

Editors:
Ronghuai Huang
Knowledge Science & Engineering Institute, School of Education Technology, Beijing Normal University, Beijing, China 100875
,
Qiang Yang
The Hong Kong University of Science and Technology, Clear Water Bay,, Hong Kong
,
Jian Pei
School of Computing Science, Simon Fraser University, Burnaby, Canada V5A 1S6
,
João Gama
Faculty of Economics, University of Porto, Porto, Portugal 4200-465
,
Xiaofeng Meng
School of Information, Zhongguancum, Renmin University, Beijing, China 100872
,
Xue Li
School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, Australia 4072

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Berlin, Heidelberg

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Published: 14 August 2009

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Oliveira GCouto da Silva AMoro M(2024)What makes a viral song? Unraveling music virality factorsProceedings of the 16th ACM Web Science Conference10.1145/3614419.3644011(181-190)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3614419.3644011
Silva MOliveira GSeufitelli DLacerda AMoro M(2022)Collaboration as a Driving Factor for Hit Song ClassificationProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3539637.3556993(66-74)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3539637.3556993
Silva MMoro Mdos Santos JMuchaluat Saade Dda Graça C. Pimentel MMacedo A(2019)Causality analysis between collaboration profiles and musical successProceedings of the 25th Brazillian Symposium on Multimedia and the Web10.1145/3323503.3349549(369-376)Online publication date: 29-Oct-2019
https://dl.acm.org/doi/10.1145/3323503.3349549
Silva MRocha LMoro MHung CPapadopoulos G(2019)Collaboration profiles and their impact on musical successProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297483(2070-2077)Online publication date: 8-Apr-2019
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Kim YSuh BLee KSchedl MKnees PShen J(2014)#nowplaying the future billboardProceedings of the first international workshop on Social media retrieval and analysis10.1145/2632188.2632206(51-56)Online publication date: 11-Jul-2014
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Carmel DRoitman HYom-Tov E(2012)On the Relationship between Novelty and Popularity of User-Generated ContentACM Transactions on Intelligent Systems and Technology10.1145/2337542.23375543:4(1-19)Online publication date: 1-Sep-2012
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Carmel DRoitman HYom-Tov EHuang JKoudas NJones GWu XCollins-Thompson KAn A(2010)On the relationship between novelty and popularity of user-generated contentProceedings of the 19th ACM international conference on Information and knowledge management10.1145/1871437.1871659(1509-1512)Online publication date: 26-Oct-2010
https://dl.acm.org/doi/10.1145/1871437.1871659

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What makes a viral song? Unraveling music virality factors

Social media and classical music?: a first analysis within the PHENICX project: "performances as highly enriched aNd interactive concert eXperiences"

Social media, traditional media, and music sales

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