Large scale analysis of gender bias and sexism in song lyrics

EPJ Data Science

卷:12

Regular Article

Carlo Abrate¹  Lorenzo Betti²  Andreas Kaltenbrunner³ 

[1] CENTAI, Corso Inghilterra 3, 10138, Turin, Italy;Sapienza University, Piazzale Aldo Moro 5, 00185, Rome, Italy;

[2] ISI Foundation, Via Chisola 5, 10126, Turin, Italy;Department of Network and Data Science, Central European University, Quellenstraße 51-55, 1100, Vienna, Austria;

[3] ISI Foundation, Via Chisola 5, 10126, Turin, Italy;Universitat Pompeu Fabra, Tanger 122, 08018, Barcelona, Catalonia, Spain;

关键词: Song lyrics;    Gender;    Natural language processing;    Word embeddings;    Language bias;    Sexism;   

DOI  :  10.1140/epjds/s13688-023-00384-8

 received in 2022-08-30, accepted in 2023-03-27,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2023

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