Victor R. Martinez, Krishna Somandepalli, and Shrikanth Narayanan. Boys don't cry (or kiss or dance): A computational linguistic lens into gendered actions in film. PLOS ONE, 17(12):1–23, Public Library of Science, 12 2022.

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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.

BibTeX Entry

@article{MartinezPLOSOne22,
    doi = {10.1371/journal.pone.0278604},
    author = {Martinez, Victor R. and Somandepalli, Krishna and Narayanan, Shrikanth},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Boys don't cry (or kiss or dance): A computational linguistic lens into gendered actions in film},
    year = {2022},
    month = {12},
    volume = {17},
    url = {https://doi.org/10.1371/journal.pone.0278604},
    pages = {1-23},
  bib2html_rescat = {mica},
 link = {http://sail.usc.edu/publications/files/Martinez-PLOSONE2022.pdf},
    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.},
    number = {12},
}

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