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

TL;DR

This study uses computational methods to analyze gender bias in English song lyrics, revealing thematic shifts over time and persistent stereotypes, with implications for understanding cultural trends and biases.

Contribution

It introduces a novel combination of topic modeling and bias measurement techniques to quantify gender bias in a large dataset of song lyrics.

Findings

Significant thematic shift from romantic to sexual themes over time

Persistent male bias in strength and intelligence words

Female bias observed in appearance and weakness words

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.