Large-scale computational content analysis on magazines targeting men and women: the case of Argentina 2008-2018

Differences in magazines content aimed specifically at women or men are a means to create and reproduce gender stereotypes. Novel computational tools allow to study differences in magazines content taking into account all available articles. In this study, we analyse the case of two Argentinian magazines published by the same publishing house over a decade (2008–2018), advertised by the publishing house as targeting women and men respectively. Using computational tools, we are able to analyse more than 24,000 articles, which would have been an impossible task using manual content analysis methodologies. With Topic Modelling techniques we identify the main themes discussed in the magazines and quantify their different frequency between magazines over time. Then, we performed a word-frequency analysis to validate this methodology and extend the analysis to other subjects. Our results show that topics such as <i>Family, Business</i> and <i>Women as sex objects</i> present an initial bias that tends to disappear over time. Conversely, in <i>Fashion</i> and <i>Science</i> topics, the initial differences are maintained. Also, we identify a considerable increase in the use of words associated with feminism since 2015 and specifically the word <i>abortion</i> in 2018. Furthermore, we develop a website where everyone can perform additional analysis.