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Music classification, genres, and taste patterns : a ground-up network analysis on the clustering of artist preferences

Jef Vlegels (UGent) and John Lievens (UGent)
(2017) POETICS. 60. p.76-89
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Abstract
This article reflects on the use of predetermined genre lists to measure patterns in music taste and, more specifically, cultural omnivorousness. The use of a predetermined array of genres assumes that music genres are rigid and stable concepts, whereas in reality genre boundaries continually emerge, evolve, and disappear. Inspired by Lamont's (2010) call to study classification systems 'from the ground up', we present an alternative strategy to measure patterns of music taste using an open question about artist preferences. We build a two-mode network of artists and respondents to identify clusters of respondents that have similar relationships to the same set of artists. Our results show that research using measurements of cultural omnivorousness based on genre preferences might be hampered, as it misses important subdivisions within genres and is not able to capture respondents who combine specific aspects within and across music genres. (C) 2016 Elsevier B.V. All rights reserved.
Keywords
CULTURAL PARTICIPATION, FIELD ANALYSIS, OMNIVORE, CONSUMPTION, HIGHBROW, OMNIVOROUSNESS, BOUNDARIES, BOURDIEU, Cultural omnivore, Music taste, Social network analysis, Two-mode network, Infinite relational model

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MLA
Vlegels, Jef, and John Lievens. “Music Classification, Genres, and Taste Patterns : A Ground-up Network Analysis on the Clustering of Artist Preferences.” POETICS, vol. 60, 2017, pp. 76–89, doi:10.1016/j.poetic.2016.08.004.
APA
Vlegels, J., & Lievens, J. (2017). Music classification, genres, and taste patterns : a ground-up network analysis on the clustering of artist preferences. POETICS, 60, 76–89. https://doi.org/10.1016/j.poetic.2016.08.004
Chicago author-date
Vlegels, Jef, and John Lievens. 2017. “Music Classification, Genres, and Taste Patterns : A Ground-up Network Analysis on the Clustering of Artist Preferences.” POETICS 60: 76–89. https://doi.org/10.1016/j.poetic.2016.08.004.
Chicago author-date (all authors)
Vlegels, Jef, and John Lievens. 2017. “Music Classification, Genres, and Taste Patterns : A Ground-up Network Analysis on the Clustering of Artist Preferences.” POETICS 60: 76–89. doi:10.1016/j.poetic.2016.08.004.
Vancouver
1.
Vlegels J, Lievens J. Music classification, genres, and taste patterns : a ground-up network analysis on the clustering of artist preferences. POETICS. 2017;60:76–89.
IEEE
[1]
J. Vlegels and J. Lievens, “Music classification, genres, and taste patterns : a ground-up network analysis on the clustering of artist preferences,” POETICS, vol. 60, pp. 76–89, 2017.
@article{8503555,
  abstract     = {This article reflects on the use of predetermined genre lists to measure patterns in music taste and, more specifically, cultural omnivorousness. The use of a predetermined array of genres assumes that music genres are rigid and stable concepts, whereas in reality genre boundaries continually emerge, evolve, and disappear. Inspired by Lamont's (2010) call to study classification systems 'from the ground up', we present an alternative strategy to measure patterns of music taste using an open question about artist preferences. We build a two-mode network of artists and respondents to identify clusters of respondents that have similar relationships to the same set of artists. Our results show that research using measurements of cultural omnivorousness based on genre preferences might be hampered, as it misses important subdivisions within genres and is not able to capture respondents who combine specific aspects within and across music genres. (C) 2016 Elsevier B.V. All rights reserved.},
  author       = {Vlegels, Jef and Lievens, John},
  issn         = {0304-422X},
  journal      = {POETICS},
  keywords     = {CULTURAL PARTICIPATION,FIELD ANALYSIS,OMNIVORE,CONSUMPTION,HIGHBROW,OMNIVOROUSNESS,BOUNDARIES,BOURDIEU,Cultural omnivore,Music taste,Social network analysis,Two-mode network,Infinite relational model},
  language     = {eng},
  pages        = {76--89},
  title        = {Music classification, genres, and taste patterns : a ground-up network analysis on the clustering of artist preferences},
  url          = {http://dx.doi.org/10.1016/j.poetic.2016.08.004},
  volume       = {60},
  year         = {2017},
}

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