Imagine a princess asleep in a castle, waiting for her prince to slay the and rescue her. Tales like the famous Sleeping Beauty clearly divide up roles. But what about more modern stories, borne of a generation aware of social constructs like sexism and racism? Do these tend to reinforce gender stereotypes, or counter them? In this paper, present a technique that combines natural language processing with a lexicon of stereotypes to capture gender biases in fiction. We this technique across 1.8 billion words of fiction from the Wattpad writing community, investigating gender representation in stories, how and female characters behave and are described, and how authors' use of stereotypes is associated with the community's ratings. We find that over-representation and traditional gender stereotypes (e.g., dominant men submissive women) are common throughout nearly every genre in our corpus., only some of these stereotypes, like sexual or violent men, are with highly rated stories. Finally, despite women often being the of negative stereotypes, female authors are equally likely to write such as men.
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Fast et al. (2016) studied this question.