Tuning Into Bias: A Computational Study of Gender Bias in Song Lyrics

• URL: http://arxiv.org/abs/2409.15949v2
• Date: Tue, 11 Mar 2025 20:54:07 GMT
• Title: Tuning Into Bias: A Computational Study of Gender Bias in Song Lyrics
• Authors: Danqing Chen, Adithi Satish, Rasul Khanbayov, Carolin M. Schuster, Georg Groh,
• Abstract summary: This paper presents an analysis of gender bias in English song lyrics using topic modeling and bias measurement techniques.<n>We cluster a dataset of 537,553 English songs into distinct topics and analyze their temporal evolution.<n>Our results reveal a significant thematic shift in song lyrics over time, transitioning from romantic themes to a heightened focus on the sexualization of women.
• Score: 1.5379084885764847
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The application of text mining methods is becoming increasingly prevalent, particularly within Humanities and Computational Social Sciences, as well as in a broader range of disciplines. This paper presents an analysis of gender bias in English song lyrics using topic modeling and bias measurement techniques. Leveraging BERTopic, we cluster a dataset of 537,553 English songs into distinct topics and analyze their temporal evolution. Our results reveal a significant thematic shift in song lyrics over time, transitioning from romantic themes to a heightened focus on the sexualization of women. Additionally, we observe a substantial prevalence of profanity and misogynistic content across various topics, with a particularly high concentration in the largest thematic cluster. To further analyse gender bias across topics and genres in a quantitative way, we employ the Single Category Word Embedding Association Test (SC-WEAT) to calculate bias scores for word embeddings trained on the most prominent topics as well as individual genres. The results indicate a consistent male bias in words associated with intelligence and strength, while appearance and weakness words show a female bias. Further analysis highlights variations in these biases across topics, illustrating the interplay between thematic content and gender stereotypes in song lyrics.

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