Large scale analysis of gender bias and sexism in song lyrics
ISI Foundation, Via Chisola 5, 10126, Turin, Italy
2 Department of Network and Data Science, Central European University, Quellenstraße 51-55, 1100, Vienna, Austria
3 CENTAI, Corso Inghilterra 3, 10138, Turin, Italy
4 Sapienza University, Piazzale Aldo Moro 5, 00185, Rome, Italy
5 Universitat Pompeu Fabra, Tanger 122, 08018, Barcelona, Catalonia, Spain
Accepted: 27 March 2023
Published online: 20 April 2023
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.
Key words: Song lyrics / Gender / Natural language processing / Word embeddings / Language bias / Sexism
© The Author(s) 2023
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