Judging a Book by its Description : Analyzing Gender Stereotypes in the Man Bookers Prize Winning Fiction
This work addresses gender bias in literature for researchers and cultural critics, though it is incremental as it applies existing methods to a new dataset.
The study quantified gender stereotypes in Man Booker Prize-winning fiction by analyzing 275 books shortlisted from 1969 to 2017, revealing pervasive bias in character occupations, introductions, and actions through semantic modeling of Goodreads descriptions.
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.