Reference : Gender bias in magazines oriented to men and women: a computational approach
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Social & behavioral sciences, psychology : Communication & mass media
Computational Sciences
Gender bias in magazines oriented to men and women: a computational approach
Kozlowski, Diego mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Lozano, Gabriela []
Fletcher, Carla M. []
Gonzalez, Fernando []
Altszyler, Edgar []
[en] Magazines ; Gender bias ; Computational Social Science
[en] Cultural products are a source to acquire individual values and behaviours. Therefore, the differences in the content of the magazines aimed specifically at women or men are a means to create and reproduce gender stereotypes. In this study, we compare the content of a women-oriented magazine with that of a men-oriented one, both produced by the same editorial group, over a decade (2008-2018). With Topic Modelling techniques we identify the main themes discussed in the magazines and quantify how much the presence of these topics differs between magazines over time. Then, we performed a word-frequency analysis to validate this methodology and extend the analysis to other subjects that did not emerge automatically. Our results show that the frequency of appearance of the topics Family, Business and Women as sex objects, present an initial bias that tends to disappear over time. Conversely, in Fashion and Science topics, the initial differences between both magazines are maintained. Besides, we show that in 2012, the content associated with horoscope increased in the women-oriented magazine, generating a new gap that remained open over time. Also, we show a strong increase in the use of words associated with feminism since 2015 and specifically the word abortion in 2018. Overall, these computational tools allowed us to analyse more than 24,000 articles. Up to our knowledge, this is the first study to compare magazines in such a large dataset, a task that would have been prohibitive using manual content analysis methodologies
Fonds National de la Recherche - FnR
FnR ; FNR12252781 > Andreas Zilian > DRIVEN > Data-driven Computational Modelling And Applications > 01/09/2018 > 28/02/2025 > 2017

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