# Using Data Science to Understand the Film Industry's Gender Gap

## TL;DR

This study uses data science to analyze gender representation in movies over the past century, revealing improvements in female roles and proposing a new evaluation method.

## Contribution

It introduces a large movie social network dataset, a new metric for female role assessment, and an open-source framework for future research.

## Key findings

- Increase in centrality of female characters over time
- More movies passing the Bechdel test
- Proposed a superior alternative to the Bechdel test

## Abstract

Data science can offer answers to a wide range of social science questions. Here we turn attention to the portrayal of women in movies, an industry that has a significant influence on society, impacting such aspects of life as self-esteem and career choice. To this end, we fused data from the online movie database IMDb with a dataset of movie dialogue subtitles to create the largest available corpus of movie social networks (15,540 networks). Analyzing this data, we investigated gender bias in on-screen female characters over the past century. We find a trend of improvement in all aspects of women`s roles in movies, including a constant rise in the centrality of female characters. There has also been an increase in the number of movies that pass the well-known Bechdel test, a popular--albeit flawed--measure of women in fiction. Here we propose a new and better alternative to this test for evaluating female roles in movies. Our study introduces fresh data, an open-code framework, and novel techniques that present new opportunities in the research and analysis of movies.