Key Female Characters in Film Have More to Talk About Besides Men: Automating the Bechdel Test

By Apoorv Agarwal, Jiehan Zheng, Shruti Kamath, Sriramkumar Balasubramanian, S. A. Dey. 2015

46 citations recorded by the source corpus.

The Bechdel test is a sequence of three questions designed to assess the presence of women in movies. Many believe that because women are seldom represented in film as strong leaders and thinkers, viewers associate weaker stereotypes with women. In this paper, we present a computational approach to automate the task of finding whether a movie passes or fails the Bechdel test. This allows us to study the key differences in language use and in the importance of roles of women in movies that pass the test versus the movies that fail the test. Our experiments confirm that in movies that fail the test, women are in fact portrayed as less-central and less-important characters.

Subfields: Artificial Intelligence, Gender Studies, Natural Language Processing, AI Ethics, Fairness & Society

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Citations in the corpus

Connotation Frames of Power and Agency in Modern Films (2017, methodology): “Agarwal et al. (2015), in particular, automate the Bechdel test using social network features, ﬁnding that women are less central to the plot in movies that fail it.”
Event2Mind: Commonsense Inference on Events, Intents, and Reactions (2018, methodology): “This is consistent with gender stereotypes that have been studied in movies in both NLP and psychology literature (Agarwal et al., 2015; Madaan et al., 2017; Pren-tice and Carranza, 2002; England et al., 2011).”
Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle* (2021, background): “As observed by Agarwal et al. (2019), most of these metrics are rather”
RtGender: A Corpus for Studying Differential Responses to Gender (2018, background): “However, previous computational work on language and gender has focused mainly on language about or portraying persons of a particular gender (Wagner et al., 2015; Flekova et al., 2016; Agarwal et al., 2015).”