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.
Original language | English |
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Article number | 92 |
Journal | Palgrave Communications |
Volume | 6 |
Issue number | 1 |
DOIs | |
State | Published - 1 Dec 2020 |
Keywords
- Computer Science - Social and Information Networks
- Computer Science - Computers and Society
- Physics - Data Analysis
- Statistics and Probability
ASJC Scopus subject areas
- General Arts and Humanities
- General Social Sciences
- General Psychology
- Economics, Econometrics and Finance (all)