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

The Arts

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

D. Kagan, T. Chesney, et al.

This groundbreaking study by Dima Kagan, Thomas Chesney, and Michael Fire explores gender bias in movies using innovative data science techniques. By analyzing IMDb data alongside movie dialogue subtitles, they reveal a promising trend towards greater representation of women in film, including increased roles and influence. Discover their new approach to evaluating female characters that surpasses the traditional Bechdel test!... show more
Citation Metrics
Citations
31
Influential Citations
1
Reference Count
64
Citation by Year

Note: The citation metrics presented here have been sourced from Semantic Scholar and OpenAlex.

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