Judging a Book by its Description : Analyzing Gender Stereotypes in the Man Bookers Prize Winning Fiction

TL;DR

This study analyzes gender stereotypes in Man Booker Prize-winning fiction from 1969 to 2017 by semantic modeling of book descriptions, revealing widespread gender bias in character roles and actions.

Contribution

It introduces a novel semantic analysis approach to quantify gender stereotypes in a large corpus of acclaimed fiction.

Findings

Gender bias is pervasive across all examined books.

Stereotypes are evident in character occupations and actions.

Semantic modeling effectively uncovers biases in literary descriptions.

Abstract

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.