The disciplines of Gender Studies and Data Science are incompatible. This is conventional wisdom, supported by how many computational studies simplify gender into an immutable binary categorization that appears crude to the critical social researcher. I argue that the characterization of gender norms is context specific and may prove valuable in constructing useful models. I show how gender can be framed in computational studies as a stylized repetition of acts mediated by a social structure, and not a possessed biological category. By conducting a review of existing work, I show how gender should be explored in multiplicity in computational research through clustering techniques, and layout how this is being achieved in a study in progress on gender hostility on Stack Overflow.

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Online anonymous forums are valued as spaces of open participation which facilitate multiple expressions of identity. In this paper I unmask how memes perpetuate existing gender power structures in spaces where users are only known by pseudonyms. The site of study is the r/AdviceAnimals forum of Reddit, in which a popular genre of memes known as Advice Animals are shared by users. Using a cross-section of Advice Animal memes submitted to this forum, the study examines the uneven distribution of power in representations of gender and feminists. I find that while women are highly sexualised, obsessive, and clingy, male characters are a default central character, used to express non-specific ideals of behaviour. As feminists, men are portrayed as legitimate, whilst women-feminists are epitomised as hypocritical, demanding, “Nazis”. The main contribution of the article is highlighting the gendered nature of memes, and call attention to the unequal representation in pseudonymous space.

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