# Quantifying Gender Bias in Consumer Culture

arXiv: https://arxiv.org/abs/2201.03173 · HTML: https://mlsanity.com/paper/2201.03173/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 59 references that are in the map.
- First posted on arXiv 10 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)
- [The Effects of Data Size and Frequency Range on Distributional Semantic Models](https://mlsanity.com/paper/1609.08293/) (2016)
- [Measuring the happiness of large-scale written expression: Songs, Blogs, and Presidents](https://mlsanity.com/paper/1703.09774/) (2017)
- [Examining the Presence of Gender Bias in Customer Reviews Using Word Embedding](https://mlsanity.com/paper/1902.00496/) (2019)

## Built on by (most cited first, 2 of 2)

- [Large scale analysis of gender bias and sexism in song lyrics](https://mlsanity.com/paper/2208.02052/) (2022)
- [Tuning Into Bias: A Computational Study of Gender Bias in Song Lyrics](https://mlsanity.com/paper/2409.15949/) (2024)

## Abstract (arXiv)

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.