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

Author

Listed:
  • Dima Kagan

    (Ben-Gurion University of the Negev)

  • Thomas Chesney

    (Nottingham University Business School)

  • Michael Fire

    (Ben-Gurion University of the Negev)

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.

Suggested Citation

  • Dima Kagan & Thomas Chesney & Michael Fire, 2020. "Using data science to understand the film industry’s gender gap," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-16, December.
    • Handle: RePEc:pal:palcom:v:6:y:2020:i:1:d:10.1057_s41599-020-0436-1
    DOI: 10.1057/s41599-020-0436-1
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    References listed on IDEAS

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    1. Sen Jia & Thomas Lansdall-Welfare & Saatviga Sudhahar & Cynthia Carter & Nello Cristianini, 2016. "Women Are Seen More than Heard in Online Newspapers," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-11, February.
    2. Vincent Larivière & Chaoqun Ni & Yves Gingras & Blaise Cronin & Cassidy R. Sugimoto, 2013. "Bibliometrics: Global gender disparities in science," Nature, Nature, vol. 504(7479), pages 211-213, December.
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    Cited by:

    1. Muhammad Junaid Haris & Aanchal Upreti & Melih Kurtaran & Filip Ginter & Sebastien Lafond & Sepinoud Azimi, 2023. "Identifying gender bias in blockbuster movies through the lens of machine learning," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-8, December.
    2. Johann Valentowitsch, 2023. "Hollywood caught in two worlds? The impact of the Bechdel test on the international box office performance of cinematic films," Marketing Letters, Springer, vol. 34(2), pages 293-308, June.

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