Quantifying Gender Bias in Consumer Culture

TL;DR

This study uses natural language processing to analyze a large dataset of songs over 50 years, quantifying misogyny and cultural stereotypes, revealing persistent biases and their evolution influenced by gender of artists.

Contribution

It provides a novel, large-scale quantitative analysis of gender bias in song lyrics over time using NLP techniques, highlighting cultural shifts and the role of male artists.

Findings

Women are less associated with competence in lyrics.

Bias against women has decreased over time.

Male artists' lyrics drive shifts in stereotypes.

Abstract

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.