Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children’s Fairy Tales

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, pages 6509–6531 July 9-14, 2023 ©2023 Association for Computational Linguistics Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children’s Fairy Tales Paulina Toro Isaza1, Guangxuan Xu1 Akintoye Oloko1 Yufang Hou1, Nanyun Peng2, Dakuo Wang3 1 IBM Research 2University of California Los Angeles 3Northeastern University {ptoroisaza, gx.xu}@ibm.com yhou@ie.ibm.com violetpeng@cs.ucla.edu d.wang@northeastern.edu Abstract Social biases and stereotypes are embedded in our culture in part through their presence in our stories, as evidenced by the rich history of hu- manities 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 con- tribution by taking into account the event narra- tive 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 anal- ysis 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. 1 Introduction Social biases and stereotypes are embedded in our culture in part through their presence in our narratives (Taylor, 2003). Despite the focus on documenting and mitigating the social bias that arises from the pre-trained embeddings used in nat- ural language processing (NLP) (Zhao et al., 2018; Kurita et al., 2019; Lu et al., 2020; Sheng et al., 2020), these methods also lend themselves to an- alyzing the biases within existing texts (Asr et al., 2021). Meanwhile, the humanities and social sci- ences have a rich history of analyzing social bias in texts such as literary works, news reports, and fairy tales (Garry, 2017). However, these analyses are often conducted manually and at a small scale. Ad- vances in natural language processing now allow for in-depth, large scale analyses of social biases within narrative texts. As storybooks, especially fairy tales, are particularly important to children’s mental, emotional, and social development (Peter- son and Lach, 1990; Narahara, 1998) , we use fairy tales as our genre of analysis. In this paper, we analyze the gender bias in children’s fairy tales by comparing the event chains of female versus male characters. Bias within the field of NLP can take on many different meanings (Blodgett et al., 2020). We adopt Blodgett et al.’s definition of social bias as representational harm through social group stereo- types. These groups can be based on social at- tributes such as …

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