Computational appraisal of gender representativeness in popular movies

Abstract Gender representation in mass media has long been mainly studied by qualitatively analyzing content. This article illustrates how automated computational methods may be used in this context to scale up such empirical observations and increase their resolution and significance. We specifical...

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Main Authors: Antoine Mazières, Telmo Menezes, Camille Roth
Format: Article
Language:English
Published: Springer Nature 2021-06-01
Series:Humanities & Social Sciences Communications
Online Access:https://doi.org/10.1057/s41599-021-00815-9
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spelling doaj-efe9fbffcaac4724936fd02789cfa4d22021-06-13T11:06:54ZengSpringer NatureHumanities & Social Sciences Communications2662-99922021-06-01811910.1057/s41599-021-00815-9Computational appraisal of gender representativeness in popular moviesAntoine Mazières0Telmo Menezes1Camille Roth2CNRS, Centre Marc Bloch, Computational Social Science TeamCNRS, Centre Marc Bloch, Computational Social Science TeamCNRS, Centre Marc Bloch, Computational Social Science TeamAbstract Gender representation in mass media has long been mainly studied by qualitatively analyzing content. This article illustrates how automated computational methods may be used in this context to scale up such empirical observations and increase their resolution and significance. We specifically apply a face and gender detection algorithm on a broad set of popular movies spanning more than three decades to carry out a large-scale appraisal of the on-screen presence of women and men. Beyond the confirmation of a strong under-representation of women, we exhibit a clear temporal trend towards fairer representativeness. We further contrast our findings with respect to a movie genre, budget, and various audience-related features such as movie gross and user ratings. We lastly propose a fine description of significant asymmetries in the mise-en-scène and mise-en-cadre of characters in relation to their gender and the spatial composition of a given frame.https://doi.org/10.1057/s41599-021-00815-9
collection DOAJ
language English
format Article
sources DOAJ
author Antoine Mazières
Telmo Menezes
Camille Roth
spellingShingle Antoine Mazières
Telmo Menezes
Camille Roth
Computational appraisal of gender representativeness in popular movies
Humanities & Social Sciences Communications
author_facet Antoine Mazières
Telmo Menezes
Camille Roth
author_sort Antoine Mazières
title Computational appraisal of gender representativeness in popular movies
title_short Computational appraisal of gender representativeness in popular movies
title_full Computational appraisal of gender representativeness in popular movies
title_fullStr Computational appraisal of gender representativeness in popular movies
title_full_unstemmed Computational appraisal of gender representativeness in popular movies
title_sort computational appraisal of gender representativeness in popular movies
publisher Springer Nature
series Humanities & Social Sciences Communications
issn 2662-9992
publishDate 2021-06-01
description Abstract Gender representation in mass media has long been mainly studied by qualitatively analyzing content. This article illustrates how automated computational methods may be used in this context to scale up such empirical observations and increase their resolution and significance. We specifically apply a face and gender detection algorithm on a broad set of popular movies spanning more than three decades to carry out a large-scale appraisal of the on-screen presence of women and men. Beyond the confirmation of a strong under-representation of women, we exhibit a clear temporal trend towards fairer representativeness. We further contrast our findings with respect to a movie genre, budget, and various audience-related features such as movie gross and user ratings. We lastly propose a fine description of significant asymmetries in the mise-en-scène and mise-en-cadre of characters in relation to their gender and the spatial composition of a given frame.
url https://doi.org/10.1057/s41599-021-00815-9
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