We employ Natural Language Processing techniques to analyse 377808 English song lyrics from the "Two Million Song Database" corpus, focusing on the expression of sexism across five decades (1960-2010) and the measurement of gender biases. Using a sexism classifier, we identify sexist lyrics at a larger scale than previous studies using small samples of manually annotated popular songs. Furthermore, we reveal gender biases by measuring associations in word embeddings learned on song lyrics. We find sexist content to increase across time, especially from male artists and for popular songs appearing in Billboard charts. Songs are also shown to contain different language biases depending on the gender of the performer, with male solo artist songs containing more and stronger biases. This is the first large scale analysis of this type, giving insights into language usage in such an influential part of popular culture.

翻译：我们采用自然语言处理技术，对来自"两百万歌曲数据库"语料库中的377808首英文歌词进行分析，重点关注1960-2010年间五十年间性别歧视的表达方式及性别偏见的量化测度。通过性别歧视分类器，我们以远超先前基于人工标注抽样流行歌曲的研究规模识别了含性别歧视的歌词。此外，我们通过测量歌词词嵌入中的语义关联揭示了性别偏见。研究发现，性别歧视内容随时间推移呈上升趋势，尤其体现在男性艺术家作品及Billboard榜单热门歌曲中。分析还表明，歌词中的语言偏见因表演者性别而异，男性独唱艺术家的歌曲包含更频繁且更强烈的偏见。这是该领域首次大规模分析，为这一极具影响力的流行文化载体中的语言使用提供了重要洞见。