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arXiv · 2409.15949

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

Danqing Chen·Adithi Satish·Rasul Khanbayov·Carolin M. Schuster·Georg Groh
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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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Danqing Chen, Adithi Satish, Rasul Khanbayov, Carolin M. Schuster, Georg Groh. 2024-09-24. Tuning Into Bias: A Computational Study of Gender Bias in Song Lyrics. https://arxiv.org/abs/2409.15949

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