Beats of Bias: Analyzing Lyrics with Topic Modeling and Gender Bias Measurements
<|reference_start|>Beats of Bias: Analyzing Lyrics with Topic Modeling and Gender Bias Measurements: This paper uses topic modeling and bias measurement techniques to analyze and determine gender bias in English song lyrics. We utilize BERTopic to cluster 537,553 English songs into distinct topics and chart their development over time. Our analysis shows the thematic shift in song lyrics over the years, from themes of romance to the increasing sexualization of women in songs. We observe large amounts of profanity and misogynistic lyrics on various topics, especially in the overall biggest cluster. Furthermore, to analyze gender bias across topics and genres, we employ the Single Category Word Embedding Association Test (SC-WEAT) to compute bias scores for the word embeddings trained on the most popular topics as well as for each genre. We find that words related to intelligence and strength tend to show a male bias across genres, as opposed to appearance and weakness words, which are more female-biased; however, a closer look also reveals differences in biases across topics.<|reference_end|>
@article{chen2024beats,
    title={Beats of Bias: Analyzing Lyrics with Topic Modeling and Gender Bias
  Measurements},
    author={Danqing Chen, Adithi Satish, Rasul Khanbayov, Carolin M. Schuster and
  Georg Groh},
    journal={arXiv preprint arXiv:2409.15949},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15949},
    primaryClass={cs.CL}
}