Identifying gender bias in blockbuster movies through the lens of machine learning

TL;DR

This study uses natural language processing and machine learning to analyze gender portrayals in movies, revealing stereotypical patterns and biases in character traits and roles.

Contribution

It introduces a novel method to convert movie dialogues into emotional representations using Plutchik's wheel of emotions, enabling automated gender bias analysis.

Findings

Men are depicted as more dominant and envious.

Women are shown in more joyful roles.

Identified stereotypical gender patterns in movies.

Abstract

The problem of gender bias is highly prevalent and well known. In this paper, we have analysed the portrayal of gender roles in English movies, a medium that effectively influences society in shaping people's beliefs and opinions. First, we gathered scripts of films from different genres and derived sentiments and emotions using natural language processing techniques. Afterwards, we converted the scripts into embeddings, i.e. a way of representing text in the form of vectors. With a thorough investigation, we found specific patterns in male and female characters' personality traits in movies that align with societal stereotypes. Furthermore, we used mathematical and machine learning techniques and found some biases wherein men are shown to be more dominant and envious than women, whereas women have more joyful roles in movies. In our work, we introduce, to the best of our knowledge, a novel technique to convert dialogues into an array of emotions by combining it with Plutchik's wheel of emotions. Our study aims to encourage reflections on gender equality in the domain of film and facilitate other researchers in analysing movies automatically instead of using manual approaches.