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Large scale analysis of gender bias and sexism in song lyrics
Betti, Lorenzo Abrate, Carlo Kaltenbrunner, Andreas
Betti, Lorenzo
Abrate, Carlo
Kaltenbrunner, Andreas
Title / Series / Name
EPJ Data Science
Publication Volume
12
Publication Issue
1
Pages
Editors
Keywords
Gender
Language bias
Natural language processing
Sexism
Song lyrics
Word embeddings
Modeling and Simulation
Computer Science Applications
Computational Mathematics
Language bias
Natural language processing
Sexism
Song lyrics
Word embeddings
Modeling and Simulation
Computer Science Applications
Computational Mathematics
Files
URI
https://hdl.handle.net/20.500.14018/29073
Abstract
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.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2023-12
Language
ISBN
Identifiers
10.1140/epjds/s13688-023-00384-8