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!