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.

通过自然语言处理和众包的刻板印象词汇表相结合的方法，该研究探讨了1.8亿个单词中男女在小说中的性别代表、描述、行为以及作者使用性别刻板印象之间的联系，并发现男性占比较高和传统的性别刻板印象在我们的语料库中几乎普遍存在，然而只有其中一些如性感或暴力的男性才会与高评价的故事相关。最后，尽管女性常常成为负面刻板印象的对象，但女性作者与男性作者一样容易写出这样的刻板印象。

赤裸上身且危险：量化一个在线小说创作社区中性别偏见的语言信号