A quantitative analysis of gender differences in movies using psycholinguistic normatives

By Anil Ramakrishna, Nikos Malandrakis, Elizabeth Staruk, Shrikanth S. Narayanan. 2015

23 citations recorded by the source corpus.

Direct content analysis reveals important details about movies including those of gender representations and potential biases. We investigate the differences between male and female character depictions in movies, based on patterns of language used. Specifically, we use an automatically generated lexicon of linguistic norms characterizing gender ladenness. We use multivariate analysis to investigate gender depictions and correlate them with elements of movie production. The proposed metric differentiates between male and female utterances and exhibits some interesting interactions with movie genres and the screenplay writer gender.

Subfields: Gender Studies, Literature and Literary Theory

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Citations in the corpus

Connotation Frames of Power and Agency in Modern Films (2017, background): “In a similar vein, Ramakrishna et al. (2015) learn word-level “gender ladenness” features by looking at the neighbors of 925 manually annotated words.”
Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle* (2021, background): “Citation Coref L.G S.G Eng L6=S 0/1 Imm Neo Sidner (1981) Y Y Y Y N − − − Bainbridge (1985) Y Y N Y − − − − Kameyama (1986) Y Y Y Y N Y Y N Mellish (1988) N Y N Y − − − − Danlos and Namer (1988) N Y N N − − − − Yoshimoto (1988) N Y N N − − − − Zock, Francopoulo, and Laroui (1988) N Y N N − − − − Pop”
A Survey on Gender Bias in Natural Language Processing (2021, background): “Gender Ladeness Lexicon [Ramakrishna et al. 2015] 10 000 Genderness Gender Predictive Lexicon [Sap et al.”