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.

本文运用自然语言处理技术分析了 377808 首来自 “Two Million Song Database” 歌曲文本，并着重研究了性别歧视在五十年来（1960 年至 2010 年）在英语歌词中的表现和测量性别偏见。我们使用了性别歧视分类器，它可以发现规模更大的歌曲歧视，比以前使用手动标注的热门歌曲小样本的研究更明确。此外，我们通过在歌曲歌词上学到的词嵌入中测量相关性来揭示性别偏见。我们发现，性别歧视在时间上有所增加，尤其是男性艺术家和在 Billboard 排行榜上出现的热门歌曲。另外，男性独唱艺术家的歌曲在性别偏见上存在更多和更强的偏见。这是第一次进行如此大规模的分析，可以为我们认识流行文化中这一般性话语的使用提供见解。