mlsanityData to 7 Oct 2026

Large scale analysis of gender bias and sexism in song lyrics

arXiv 2208.02052 ↗First posted 3 Aug 2022cs.CY cs.CLcited by 4

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Abstract (arXiv)

We employ Natural Language Processing techniques to analyse 377808 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 facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 61 references that are in the map. First posted on arXiv 3 Aug 2022.

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16 of 61 references.

References · 16

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  12. 2021A Quantum Natural Language Processing Approach to Musical Intelligencecited by 11
4 more
  1. 2021SWEAT: Scoring Polarization of Topics across Different Corporacited by 5
  2. 2019It's Only Words And Words Are All I Havecited by 3
  3. 2019Decoding the Style and Bias of Song Lyricscited by 2
  4. 2022Quantifying Gender Bias in Consumer Culturecited by 2

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Citing papers per year: 2024: 3; 2025: 1.

4 papers.

  1. 2024ML-EAT: A Multilevel Embedding Association Test for Interpretable and Transparent Social Sciencecited by 3
  2. 2024Computational lexical analysis of Flamenco genrescited by 0
  3. 2024Tuning Into Bias: A Computational Study of Gender Bias in Song Lyricscited by 0
  4. 2025Language Models That Walk the Talk: A Framework for Formal Fairness Certificatescited by 0