Abstract
Online music communities reflect and influence people’s music tastes, providing a detailed digital record of individuals’ behavior. There have been extensive studies on human musical preference; however, the further questions of how the preferences correlate, how intensive and extensive a musical preference can spread in social networks and an individual’s preference is like his/her neighbors, and which factors are most relevant to the diversity of individual music preference are not well explored. In this paper, we analyze the music preference of users in a large online music community in China. We find that there exists obvious correlated musical preference for certain pairs of genres or languages. The preference locality leads to the decay of preference similarity between users and their neighbors with network distance, and the decay is asymmetric for fans and followees in terms of preference probability, preference distribution similarity and list similarity. Users’ musical preference well reflects their ethnic, cultural, and demographic features. We quantify preference diversity, reveal the factors which are significantly correlated with or can predict the diversity, and find that the mean diversity of users’ nearest fans can be the most important predictor. This study reveals the characteristics of users’ music preference, producing new sights on musical tastes, their diversity determinants, and their correlation with surrounding communities and cultures.
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References
Askin N, Mauskapf M (2017) What makes popular culture popular? Product features and optimal differentiation in music. Am Sociol Rev 82:910–944. https://doi.org/10.1177/0003122417728662
Baym NK, Ledbetter A (2009) Tunes that bind? Predicting friendship strength in a music-based social network. Inform, Commun Soc 12:408–427. https://doi.org/10.1080/13691180802635430
Bliss CA, Kloumann IM, Harris KD, Danforth CM, Dodds PS (2012) Twitter reciprocal reply networks exhibit assortativity with respect to happiness. J Comput Sci 3:388–397. https://doi.org/10.1016/j.jocs.2012.05.001
Bonneville-Roussy A, Stillwell D, Kosinski M, Rust J (2017) Age trends in musical preferences in adulthood: 1. Conceptualization and empirical investigation. Musicae Scientiae 21:369–389. https://doi.org/10.1177/1029864917691571
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Chen H, De P, Hu YJ (2015) IT-enabled broadcasting in social media: an empirical study of artists’ activities and music sales. Inform Syst Res 26:513–531. https://doi.org/10.1287/isre.2015.0582
Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 785–794. https://doi.org/10.1145/2939672.2939785
Christakis NA, Fowler JH (2013) Social contagion theory: examining dynamic social networks and human behavior. Statist Med 32:556–577. https://doi.org/10.1002/sim.5408
Crosby P, Lenten LJA, Mckenzie J (2018) Social media followers as music fans: analysis of a music poll event. Econ Lett 168:85–89. https://doi.org/10.1016/j.econlet.2018.04.024
Datta H, Knox G, Bronnenberg BJ (2018) Changing their tune: how consumers’ adoption of online streaming affects music consumption and discovery. Market Sci 37:5–21. https://doi.org/10.1287/mksc.2017.1051
Dewan S, Ho Y, Ramaprasad J (2017) Popularity or proximity: characterizing the nature of social influence in an online music community. Inform Syst Res 28:117–136. https://doi.org/10.1287/isre.2016.0654
Fan R, Zhao J, Chen Y, Xu K (2014) Anger is more influential than joy: sentiment correlation in Weibo. PLoS ONE 9:e110184. https://doi.org/10.1371/journal.pone.0110184
Fricke KR, Greenberg DM, Rentfrow PJ, Herzberg PY (2018) Computer-based music feature analysis mirrors human perception and can be used to measure individual music preference. J Res Personal 75:94–102. https://doi.org/10.1016/j.jrp.2018.06.004
Gu H, Wang J, Wang Z, Zhuang B, Bian W, Su F (2018) Cross-platform modeling of users’ behavior on social media. In: IEEE International Conference on Data Mining Workshops. pp. 183–190. https://doi.org/10.1109/ICDMW.2018.00035
Haampland O (2017) Power laws and market shares: cumulative advantage and the billboard hot 100. J New Music Res 46:356–380. https://doi.org/10.1080/09298215.2017.1358285
Hu H, Han D (2008) Empirical analysis of individual popularity and activity on an online music service system. Physica A 387:5916–5921. https://doi.org/10.1016/j.physa.2008.06.018
Hu H, Han D, Wang X (2010) Individual popularity and activity in online social systems. Physica A 389:1065–1070. https://doi.org/10.1016/j.physa.2009.11.007
Júnior JFS-Q, Lorenzo O, Herrera L, Santos NSA (2019) Gender and religion as factors of individual differences in musical preference. Musicae Scientiae 23:525–539. https://doi.org/10.1177/1029864918774834
Klimek P, Kreuzbauer R, Thurner S (2019) Fashion and art cycles are driven by counter-dominance signals of elite competition: quantitative evidence from music styles. J R Soc Interface 16:20180731. https://doi.org/10.1098/rsif.2018.0731
Koch NM, Soto IM (2016) Let the music be your master: power laws and music listening habits. Musicae Scientiae 20:193–206. https://doi.org/10.1177/1029864915619000
Lambert B, Kontonatsios G, Mauch M, Kokkoris T, Jockers M, Ananiadou S, Leroi AM (2020) The pace of modern culture. Nat Human Behav 4:352–360. https://doi.org/10.1038/s41562-019-0802-4
Lee M, Choi HB, Cho D, Lee H (2016) Cannibalizing or complementing? The impact of online streaming services on music record sales. Procedia Comput Sci 91:662–671. https://doi.org/10.1016/j.procs.2016.07.166
Lewis K, Kaufman J (2018) The conversion of cultural tastes into social network ties. Am J Sociol 123:1684–1742. https://doi.org/10.1086/697525
Li H, Han XP, Lü L, Pan Z (2018) Measuring diversity of music tastes in online musical society. Int J Modern Phys C 29:1840006. https://doi.org/10.1142/S0129183118400065
Liebman E, Saar-Tsechansky M, Stone P (2019) The right music at the right time: adaptive personalized playlists based on sequence modeling. MIS Q. 43:765–786. https://doi.org/10.25300/MISQ/2019/14750
Marshall SR, Naumann LP (2018) What’s your favorite music? Music preferences cue racial identity. J Res Personal 76:74–91. https://doi.org/10.1016/j.jrp.2018.07.008
Monechi B, Gravino P, Servedio VDP, Tria F, Loreto V (2017) Significance and popularity in music production. R Soc Open Sci 4:170433. https://doi.org/10.1098/rsos.170433
Nave G, Minxha J, Greenberg DM, Kosinski M, Stillwell D, Rentfrow J (2018) Musical preferences predict personality: evidence from active listening and Facebook likes. Psychol Sci 29:1145–1158. https://doi.org/10.1177/0956797618761659
Park M, Thom J, Mennicken S, Cramer H, Macy M (2019) Global music streaming data reveal diurnal and seasonal patterns of affective preference. Nat Human Behav 3:230–236. https://doi.org/10.1038/s41562-018-0508-z
Park M, Weber I, Naaman M, Vieweg S (2017) Understanding musical diversity via online social media. arXiv:1604.02522
Perc M (2020) Beauty in artistic expressions through the eyes of networks and physics. J R Soc Interface 17:20190686. https://doi.org/10.1098/rsif.2019.0686
Pereira FSF, Gama J, de Amo S, Oliveira GMB (2018) On analyzing user preference dynamics with temporal social networks. Mach Learn 107:1745–1773. https://doi.org/10.1007/s10994-018-5740-2
Pichl M, Zangerle E, Specht G (2014) Combining Spotify and Twitter data for generating a recent and public dataset for music recommendation. In: Proceedings of the 26th Workshop Grundlagen von Datenbanken. pp. 35–40
Pluchino A, Biondo AE, Rapisarda A (2018) Talent versus luck: the role of randomness in success and failure. Advs Complex Syst 21:1850014. https://doi.org/10.1142/S0219525918500145
Pongnumkul S, Motohashi K (2018) A bipartite fitness model for online music streaming services. Physica A 490:1125–1137. https://doi.org/10.1016/j.physa.2017.08.108
Schneider L, Gros C (2019) Five decades of US, UK, German and Dutch music charts show that cultural processes are accelerating. R Soc Open Sci 6:190944. https://doi.org/10.1098/rsos.190944
Sordo M, Gouyon F, Sarmento L, Celma Ò, Serra X (2013) Inferring semantic facets of a music folksonomy with Wikipedia. J New Music Res 42:346–363. https://doi.org/10.1080/09298215.2013.848904
Sornette D, Wheatley S, Cauwels P (2019) The fair reward problem: the illusion of success and how to solve it. Advs Complex Syst 22:1950005. https://doi.org/10.1142/S021952591950005X
Stirling A (2007) A general framework for analysing diversity in science, technology and society. J R Soc Interface 4:707–719. https://doi.org/10.1098/rsif.2007.0213
Waldfogel J (2017) How digitization has created a golden age of music, movies, books, and television. J Econ Perspect 31:195–214. https://doi.org/10.1257/jep.31.3.195
Way SF, Gil S, Anderson I, Clauset A (2019) Environmental changes and the dynamics of musical identity. In: Proceedings of the 13th International AAAI Conference on Web and Social Media. pp. 527–536
Zheng E, Kondo GY, Zilora S, Yu Q (2018) Tag-aware dynamic music recommendation. Expert Syst Appl 106:244–251. https://doi.org/10.1016/j.eswa.2018.04.014
Zhou Z, Xu K, Zhao J (2018) Homophily of music listening in online social networks of China. Soc Netw 55:160–169. https://doi.org/10.1016/j.socnet.2018.07.001
Acknowledgements
We would like to thank anonymous referees for crucial comments and suggestions that helped us to improve the quality of the paper. The study was partly supported by the National Natural Science Foundation of China (Grant Number: 61973121).
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The dataset supporting the paper and the supplementary material are available via the following link: https://doi.org/10.6084/m9.figshare.12738224.
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Zhang, J., Hu, H. Musical preference in an online music community in China. Soc. Netw. Anal. Min. 12, 36 (2022). https://doi.org/10.1007/s13278-022-00866-z
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DOI: https://doi.org/10.1007/s13278-022-00866-z