  Cultural items like songs have an important impact in creating and
reinforcing stereotypes, biases, and discrimination. But the actual nature of
such items is often less transparent. Take songs, for example. Are lyrics
biased against women? And how have any such biases changed over time? Natural
language processing of a quarter of a million songs over 50 years quantifies
misogyny. Women are less likely to be associated with desirable traits (i.e.,
competence), and while this bias has decreased, it persists. Ancillary analyses
further suggest that song lyrics may help drive shifts in societal stereotypes
towards women, and that lyrical shifts are driven by male artists (as female
artists were less biased to begin with). Overall, these results shed light on
cultural evolution, subtle measures of bias and discrimination, and how natural
language processing and machine learning can provide deeper insight into
stereotypes and cultural change.

Computation and Language