Using data science to understand the film industry’s gender gap

Kagan, Dima; Chesney, Thomas; Fire, Michael

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.

Citation

Kagan, D., Chesney, T., & Fire, M. (2020). Using data science to understand the film industry’s gender gap. Palgrave Communications, 6, 1-16. https://doi.org/10.1057/s41599-020-0436-1

• Journal Article Type: Article
• Acceptance Date: Mar 11, 2020
• Online Publication Date: May 13, 2020
• Publication Date: 2020
• Deposit Date: Apr 7, 2020
• Publicly Available Date: May 14, 2020
• Journal: Palgrave Communications
• Electronic ISSN: 2055-1045
• Publisher: Palgrave Macmillan
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Article Number: 92
• Pages: 1-16
• DOI: https://doi.org/10.1057/s41599-020-0436-1
• Keywords: Data Science; Network Science; Gender Gap; Social Networks
• Public URL: https://nottingham-repository.worktribe.com/output/4264715
• Publisher URL: https://www.nature.com/articles/s41599-020-0436-1
• Additional Information: Received: 25 September 2019; Accepted: 11 March 2020; First Online: 13 May 2020; : The authors declare no competing interests.