Large scale analysis of gender bias and sexism in song lyrics

We employ Natural Language Processing techniques to analyse 377,808 English song lyrics from the “Two Million Song Database” corpus, focusing on the expression of sexism across five decades (1960–2010) and the measurement of gender biases. Using a sexism classifier, we identify sexist lyrics at a la...

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Detalhes bibliográficos
Autores: Betti, Lorenzo, Abrate, Carlo, Kaltenbrunner, Andreas
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2023
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositório:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/57420
Acesso em linha:http://hdl.handle.net/10230/57420
http://dx.doi.org/10.1140/epjds/s13688-023-00384-8
Access Level:Acceso aberto
Palavra-chave:Song lyrics
Gender
Natural language processing
Word embeddings
Language bias
Sexism
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spelling Large scale analysis of gender bias and sexism in song lyricsBetti, LorenzoAbrate, CarloKaltenbrunner, AndreasSong lyricsGenderNatural language processingWord embeddingsLanguage biasSexismWe employ Natural Language Processing techniques to analyse 377,808 English song lyrics from the “Two Million Song Database” corpus, focusing on the expression of sexism across five decades (1960–2010) and the measurement of gender biases. Using a sexism classifier, we identify sexist lyrics at a larger scale than previous studies using small samples of manually annotated popular songs. Furthermore, we reveal gender biases by measuring associations in word embeddings learned on song lyrics. We find sexist content to increase across time, especially from male artists and for popular songs appearing in Billboard charts. Songs are also shown to contain different language biases depending on the gender of the performer, with male solo artist songs containing more and stronger biases. This is the first large scale analysis of this type, giving insights into language usage in such an influential part of popular culture.Springer202320232023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/57420http://dx.doi.org/10.1140/epjds/s13688-023-00384-8reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésEPJ Data Science. 2023;12:10.© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/574202026-05-29T05:05:01Z
dc.title.none.fl_str_mv Large scale analysis of gender bias and sexism in song lyrics
title Large scale analysis of gender bias and sexism in song lyrics
spellingShingle Large scale analysis of gender bias and sexism in song lyrics
Betti, Lorenzo
Song lyrics
Gender
Natural language processing
Word embeddings
Language bias
Sexism
title_short Large scale analysis of gender bias and sexism in song lyrics
title_full Large scale analysis of gender bias and sexism in song lyrics
title_fullStr Large scale analysis of gender bias and sexism in song lyrics
title_full_unstemmed Large scale analysis of gender bias and sexism in song lyrics
title_sort Large scale analysis of gender bias and sexism in song lyrics
dc.creator.none.fl_str_mv Betti, Lorenzo
Abrate, Carlo
Kaltenbrunner, Andreas
author Betti, Lorenzo
author_facet Betti, Lorenzo
Abrate, Carlo
Kaltenbrunner, Andreas
author_role author
author2 Abrate, Carlo
Kaltenbrunner, Andreas
author2_role author
author
dc.subject.none.fl_str_mv Song lyrics
Gender
Natural language processing
Word embeddings
Language bias
Sexism
topic Song lyrics
Gender
Natural language processing
Word embeddings
Language bias
Sexism
description We employ Natural Language Processing techniques to analyse 377,808 English song lyrics from the “Two Million Song Database” corpus, focusing on the expression of sexism across five decades (1960–2010) and the measurement of gender biases. Using a sexism classifier, we identify sexist lyrics at a larger scale than previous studies using small samples of manually annotated popular songs. Furthermore, we reveal gender biases by measuring associations in word embeddings learned on song lyrics. We find sexist content to increase across time, especially from male artists and for popular songs appearing in Billboard charts. Songs are also shown to contain different language biases depending on the gender of the performer, with male solo artist songs containing more and stronger biases. This is the first large scale analysis of this type, giving insights into language usage in such an influential part of popular culture.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/57420
http://dx.doi.org/10.1140/epjds/s13688-023-00384-8
url http://hdl.handle.net/10230/57420
http://dx.doi.org/10.1140/epjds/s13688-023-00384-8
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv EPJ Data Science. 2023;12:10.
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
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