  Social biases and stereotypes are embedded in our culture in part through
their presence in our stories, as evidenced by the rich history of humanities
and social science literature analyzing such biases in children stories.
Because these analyses are often conducted manually and at a small scale, such
investigations can benefit from the use of more recent natural language
processing methods that examine social bias in models and data corpora. Our
work joins this interdisciplinary effort and makes a unique contribution by
taking into account the event narrative structures when analyzing the social
bias of stories. We propose a computational pipeline that automatically
extracts a story's temporal narrative verb-based event chain for each of its
characters as well as character attributes such as gender. We also present a
verb-based event annotation scheme that can facilitate bias analysis by
including categories such as those that align with traditional stereotypes.
Through a case study analyzing gender bias in fairy tales, we demonstrate that
our framework can reveal bias in not only the unigram verb-based events in
which female and male characters participate but also in the temporal narrative
order of such event participation.
