mlsanityData to 7 Oct 2026

A Moral- and Event- Centric Inspection of Gender Bias in Fairy Tales at A Large Scale

arXiv 2211.14358 ↗First posted 25 Nov 2022cs.CLcited by 3

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Abstract (arXiv)

Fairy tales are a common resource for young children to learn a language or understand how a society works. However, gender bias, e.g., stereotypical gender roles, in this literature may cause harm and skew children's world view. Instead of decades of qualitative and manual analysis of gender bias in fairy tales, we computationally analyze gender bias in a fairy tale dataset containing 624 fairy tales from 7 different cultures. We specifically examine gender difference in terms of moral foundations, which are measures of human morality, and events, which reveal human activities associated with each character. We find that the number of male characters is two times that of female characters, showing a disproportionate gender representation. Our analysis further reveal stereotypical portrayals of both male and female characters in terms of moral foundations and events. Female characters turn out more associated with care-, loyalty- and sanctity- related moral words, while male characters are more associated with fairness- and authority- related moral words. Female characters' events are often about emotion (e.g., weep), appearance (e.g., comb), household (e.g., bake), etc.; while male characters' events are more about profession (e.g., hunt), violence (e.g., destroy), justice (e.g., judge), etc. Gender bias in terms of moral foundations shows an obvious difference across cultures. For example, female characters are more associated with care and sanctity in high uncertainty-avoidance cultures which are less open to changes and unpredictability. Based on the results, we propose implications for children's literature and early literacy research.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 12 of its 36 references that are in the map. First posted on arXiv 25 Nov 2022.

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12 of 36 references.

References · 12

  1. 2019Mitigating Gender Bias in Natural Language Processing: Literature Reviewcited by 287
  2. 2021The Values Encoded in Machine Learning Researchcited by 172
  3. 2021A Survey of Race, Racism, and Anti-Racism in NLPcited by 74
  4. 2020Unsupervised Discovery of Implicit Gender Biascited by 34
  5. 2021Uncovering Implicit Gender Bias in Narratives through Commonsense Inferencecited by 32
  6. 2021Men Are Elected, Women Are Married: Events Gender Bias on Wikipediacited by 30
  7. 2021EventPlus: A Temporal Event Understanding Pipelinecited by 17
  8. 2022AI Ethics Issues in Real World: Evidence from AI Incident Databasecited by 14
  9. 2021Mapping Moral Valence of Tweets Following the Killing of George Floydcited by 4
  10. 2021Pretty Princess vs. Successful Leader: Gender Roles in Greeting Card Messagescited by 2
  11. 2022Anonymous Expression in an Online Community for Women in Chinacited by 2
  12. 2022More Than a Wife and a Mom: A Study of Mom Vlogging Practices in Chinacited by 2

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Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.

3 papers.