Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community

TL;DR

This study uses NLP and crowdsourced data to analyze gender bias in 1.8 billion words of Wattpad fiction, revealing prevalent stereotypes and their correlation with story ratings across genres.

Contribution

Introduces a novel NLP technique combined with crowdsourced stereotypes to quantify gender bias in large-scale online fiction datasets.

Findings

Male over-representation and traditional stereotypes are widespread.

Certain stereotypes, like sexual or violent men, correlate with higher ratings.

Female authors are equally likely to write stereotypes as male authors.

Abstract

Imagine a princess asleep in a castle, waiting for her prince to slay the dragon and rescue her. Tales like the famous Sleeping Beauty clearly divide up gender roles. But what about more modern stories, borne of a generation increasingly aware of social constructs like sexism and racism? Do these stories tend to reinforce gender stereotypes, or counter them? In this paper, we present a technique that combines natural language processing with a crowdsourced lexicon of stereotypes to capture gender biases in fiction. We apply this technique across 1.8 billion words of fiction from the Wattpad online writing community, investigating gender representation in stories, how male and female characters behave and are described, and how authors' use of gender stereotypes is associated with the community's ratings. We find that male over-representation and traditional gender stereotypes (e.g., dominant men and submissive women) are common throughout nearly every genre in our corpus. However, only some of these stereotypes, like sexual or violent men, are associated with highly rated stories. Finally, despite women often being the target of negative stereotypes, female authors are equally likely to write such stereotypes as men.