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© 2022 Martinez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Contemporary media is full of images that reflect traditional gender notions and stereotypes, some of which may perpetuate harmful gender representations. In an effort to highlight the occurrence of these adverse portrayals, researchers have proposed machine-learning methods to identify stereotypes in the language patterns found in character dialogues. However, not all of the harmful stereotypes are communicated just through dialogue. As a complementary approach, we present a large-scale machine-learning framework that automatically identifies character’s actions from scene descriptions found in movie scripts. For this work, we collected 1.2+ million scene descriptions from 912 movie scripts, with more than 50 thousand actions and 20 thousand movie characters. Our framework allow us to study systematic gender differences in movie portrayals at a scale. We show this through a series of statistical analyses that highlight differences in gender portrayals. Our findings provide further evidence to claims from prior media studies including: (i) male characters display higher agency than female characters; (ii) female actors are more frequently the subject of gaze, and (iii) male characters are less likely to display affection. We hope that these data resources and findings help raise awareness on portrayals of character actions that reflect harmful gender stereotypes, and demonstrate novel possibilities for computational approaches in media analysis.

Details

Title
Boys don’t cry (or kiss or dance): A computational linguistic lens into gendered actions in film
Author
Martinez, Victor R  VIAFID ORCID Logo  ; Somandepalli, Krishna; Narayanan, Shrikanth  VIAFID ORCID Logo 
First page
e0278604
Section
Research Article
Publication year
2022
Publication date
Dec 2022
Publisher
Public Library of Science
e-ISSN
19326203
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2756654198
Copyright
© 2022 Martinez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.