Judging a Book by its Description : Analyzing Gender Stereotypes in the Man Bookers Prize Winning Fiction
By Nishtha Madaan, Sameep Mehta, Shravika Mittal, Ashima Suvarna. 2018
4 citations recorded by the source corpus.
The presence of gender stereotypes in many aspects of society is a well-known phenomenon. In this paper, we focus on studying and quantifying such stereotypes and bias in the Man Bookers Prize winning fiction. We consider 275 books shortlisted for Man Bookers Prize between 1969 and 2017. The gender bias is analyzed by semantic modeling of book descriptions on Goodreads. This reveals the pervasiveness of gender bias and stereotype in the books on different features like occupation, introductions and actions associated to the characters in the book.
Subfields: Artificial Intelligence, Gender Studies, Literature and Literary Theory
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In Artificial Intelligence
Citations in the corpus
- Anonymized BERT: An Augmentation Approach to the Gendered Pronoun Resolution Challenge (2019, background): “Gender bias has been an important topic in nat-ural language processing in recent years (Boluk-basi et al., 2016; Reddy and Knight, 2016; Chi-appa and Gillam, 2018; Madaan et al., 2018).”