  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.
