分析53万首英文歌曲，发现女性被过度性化且男性更常关联智力与力量
Tuning Into Bias: A Computational Study of Gender Bias in Song Lyrics
- 用BERTopic对53万首歌分主题，追踪歌词主题随时间演变
- 女性相关词多关联外貌与脆弱，男性词则多关联智力与力量
- 性化与贬损内容集中于最大主题，且跨流派普遍存在
文本挖掘在人文学科与计算社会科学中日益普及。本文利用主题建模与偏见测量技术，分析英语歌曲歌词中的性别偏见。通过BERTopic将537,553首英文歌曲聚类为多个主题，并分析其随时间演变。结果表明，歌词主题显著转向浪漫之外的性化表达，尤其聚焦女性。此外，各类主题中普遍存在脏话与厌女内容，尤以最大主题最为集中。为进一步量化不同主题与流派中的性别偏见，我们采用单类别词嵌入关联测试（SC-WEAT），对主要主题及个别流派训练的词嵌入进行偏见评分。结果显示，与智力和力量相关的词呈现持续男性偏见，而外貌与弱点相关词则呈现女性偏见。进一步分析显示，这些偏见在不同主题间存在差异，揭示了歌词主题内容与性别刻板印象之间的复杂互动。
原文摘要 · Abstract (English)
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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