Tuning Into Bias: A Computational Study of Gender Bias in Song Lyrics (2025)

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.

## introduction disclaimer: lyrics in the dataset may include explicit or vulgar language, which is inherently reflected in the topic labels generated by the bertopic model. this does not represent the views or opinions of the authors. music is integrally tied with gender identity, where lyrics, melodies, and performance styles can reflect and shape societal perceptions of gender roles, stereotypes, and experiences @xcite @xcite . through lyrics, artists have a way of expressing their emotions and discussing unique themes. while these themes often span a wide variety of issues, they can also propagate dangerous stereotypes and objectification @xcite @xcite , pointing out the need to critically examine these gender biases that can occur in lyrics. natural language processing (nlp) techniques provide a robust framework for analyzing song lyrics by leveraging their underlying textual structure to extract thematic patterns and genderassociated linguistic representations @xcite . in particular, word embeddings @xcite , which encode lexical items as dense, highdimensional vectors within a continuous space, have been shown to effectively capture and encode latent linguistic biases that align with human cognitive associations @xcite . this representational property renders word embeddings a powerful com-putational tool for systematically quantifying and analyzing gender biases embedded within lyrical discourse @xcite . while previous research has primarily analyzed gender bias at the artist level by comparing the lyrics of songs performed by male and female artists @xcite @xcite , this study does not differentiate based on the artist's gender. instead, we focus solely on examining bias within the lyrics themselves. by integrating topic modelling with quantitative bias measurement, this approach facilitates a granular analysis of gender bias across themes and genres, utilizing nlp to bridge the gap in humanities and social sciences to analyze complex text-based artefacts and their sociocultural implications. topic modeling is a powerful technique for uncovering the underlying themes within a corpus, such as song lyrics in our study @xcite . in this paper, we employ bertopic (grootendorst, 2022), a state-of-the-art topic modeling method, to analyze persistent lyrical themes across various genres and examine their evolution over multiple decades. this approach enables us to uncover critical insights, including the increasing sexualization of women in song lyrics over time and the notable prevalence of profanity, particularly in rap music. while the topic model provides a broad overview of the gender bias in lyrics, we also take a more fine-grained look into this bias by applying the sc-weat analysis to quantify it and evaluate the associations of specific target word sets with gender-related attributes @xcite . our major contributions, as depicted in the workflow diagram in figure 1 , are: ## related work the intersection of music and natural language processing (nlp) has been the focus of extensive research, encompassing tasks such as mood classification, music transcription, lyrics and melody generation, among others @xcite @xcite . music -and, by extension, lyrics -constitutes a valuable resource for investigating underlying societal dynamics, particularly in the context of gender stereotypes and objectification @xcite @xcite . previous research has demonstrated that word embeddings are inherently susceptible to capturing and, in some cases, amplifying the social biases present in the data from which they are derived @xcite . a wellknown example provided by @xcite illustrates that the word embedding for "man" is more closely associated with "programmer," while "woman" is linked to "homemaker." similarly, the findings of @xcite and @xcite reveal that word embeddings encode implicit cultural and gender biases, even when such biases are not explicitly stated in the source data. this body of work highlights the critical importance of examining and addressing biases embedded in linguistic representations, especially when applied to cultural artifacts such as song lyrics. in our paper, we quantify this gender bias using an extension of the word embedding association test (weat), the single category weat score (sc-weat) @xcite @xcite . the sc-weat score is also used by @xcite and @xcite to analyze the nature of gender bias in lyrics and the differences across artist genders. however, we expand on this approach by using topic modeling to identify popular and intriguing topics. we then analyze the gender bias in the lyrics on a per-topic as well as per-genre basis, aiming to uncover how this bias may vary across different themes. topic modeling is a widely used technique for clustering documents to summarize or classify them, enabling the identification of underlying social patterns within the data @xcite . when applied to song lyrics, it serves as an effective approach for uncovering recurring themes @xcite @xcite . while latent dirichlet allocation (lda) remains one of the most common methods for topic modeling, recent findings by @xcite demonstrate that bertopic, introduced by @xcite , outperforms traditional approaches by producing more distinctive and interpretable clusters. bertopic has also been successfully applied in gender and social science research. for example, nakajima wickham (2023) utilized the algorithm to examine gender expectations on social media and their influence on suicidal ideation. this demonstrates bertopic's utility in the clustering of categories that are meaningful to societal and cultural dynamics. ## conclusion as a socio-cultural artefact, music offers insights into societal norms and biases, making it a valuable subject for computational analysis. this study leverages bertopic, an advanced topic modeling technique, to identify thematic patterns and gender bias in song lyrics across five genres-country, pop, rap, r&b, and rock-over 70 years. using sc-weat, we quantify gender bias within these themes and explore how biases vary across topics and genres. by addressing the intersection of music, culture, and societal norms, our findings reveal the gendered narratives embedded in song lyrics and their evolution over time. we employ a stratified sampling strategy for bertopic model training to ensure balanced genre representation. the most dominant topic, "nigga_niggas_bitch", exhibits a high prevalence of misogynistic language and profanity, becoming particularly prominent in the 1990s despite the dataset spanning from the 1950s to the 2020s. in contrast, earlier dominant themes, such as "tears_heart_wish" and "won-derful_sweeter_years_sweeter", primarily reflect p le a s a n t u n p le a s a n t a p p e a r a n c e in t e ll ig e n c e s t r e n g t h w e a k n e s s target set 1 0 1 effect size overall nigga_niggas_bitch (@xmath0.0 0.5 0.0 0.5 1.0 effect size country (@xmath1: comparison of sc-weat plots for "tears_heart_wish" in the country, pop, and r&b genres. a positive score indicates male bias, whereas a negative score indicates female bias, and n is the number of word vectors. the sc-weat analysis further examines the trends of sexualization and profanity previously identified through topic modeling. the results reveal implicit gender bias in song lyrics, with weakness and appearance words showing a female bias, while intelligence and strength words exhibit a male bias. the female bias in appearance words supports observations on the sexualiza-tion of women in music @xcite @xcite . the per-topic and per-genre analysis uncovers notable variations, with biases differing across themes and genres. for instance, in the topic "tears_heart_wish," bias scores vary across genres: country exhibits a female bias across all target sets, while intelligence words in pop and appearance words in r&b show a male bias. these results highlight the intersection of thematic content, genre, and gender bias, emphasizing the value of computational methods in analyzing sociocultural dynamics in song lyrics. in conclusion, this study demonstrates the utility of integrating topic modeling with bias measure-ment techniques to analyze thematic structures in song lyrics and examine how these themes perpetuate implicit gender biases. by applying nlp methods to a significant sociocultural dataset, this work aligns with the growing demand in digital humanities and social sciences for tools that facilitate the analysis and interpretation of complex, non-standard textual data. our approach highlights the potential of computational methods to address sociocultural questions, offering insights into how gender stereotypes are embedded in and perpetuated through lyrical content.