Capturing readers' engagement in fiction is a challenging but important
aspect of narrative understanding. In this study, we collected 23 readers'
reactions to 2 short stories through eye tracking, sentence-level annotations,
and an overall engagement scale survey. We analyzed the significance of various
qualities of the text in predicting how engaging a reader is likely to find it.
As enjoyment of fiction is highly contextual, we also investigated individual
differences in our data. Furthering our understanding of what captivates
readers in fiction will help better inform models used in creative narrative
generation and collaborative writing tools.

本研究使用眼动仪、句子级注释和整体参与度调查，分析 23 名读者对两篇短篇小说的反应及其与文本质量之间的关系，旨在进一步了解小说中读者的参与度，以更好地推动创作式叙事生成和协作写作工具的发展。

通过眼动追踪和语言特征分析文学小说读者的参与度

An Analysis of Reader Engagement in Literary Fiction through Eye  Tracking and Linguistic Features

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。