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

arXiv: https://arxiv.org/abs/2409.15949 · HTML: https://mlsanity.com/paper/2409.15949/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Conditional LSTM-GAN for Melody Generation from Lyrics](https://mlsanity.com/paper/1908.05551/) (2019)
- [Melody-Conditioned Lyrics Generation with SeqGANs](https://mlsanity.com/paper/2010.14709/) (2020)
- [Large scale analysis of gender bias and sexism in song lyrics](https://mlsanity.com/paper/2208.02052/) (2022)
- [Quantifying Gender Bias in Consumer Culture](https://mlsanity.com/paper/2201.03173/) (2022)
- [Uncovering Gender Stereotypes in Video Game Character Designs: A Multi-Modal Analysis of Honor of Kings](https://mlsanity.com/paper/2311.14226/) (2023)

## Abstract (arXiv)

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.