Gender Stereotypes in User-Generated Content - Anna Kerkhof; Valentin Reich (Center for Economic Studies and ifo Institute (CESifo), 2023)
Gender stereotypes pose an important hurdle on the way to gender equality. It is difficult to quantify the problem, though, as stereotypical beliefs are often subconscious or not openly expressed. User-generated content (UGC) opens up novel opportunities to overcome such challenges, as the anonymity of users may eliminate social pressures. This paper leverages over a million anonymous comments from a major German online discussion forum to study the prevalence and development of gender stereotypes over almost a decade. To that end, we develop an innovative and widely applicable text analysis procedure that overcomes conceptual challenges that arise whenever two variables in the training data are correlated, and changes in that correlation in the prediction sample are subject of examination themselves. Here, we apply the procedure to study the correlation between gender (i.e., does a comment discuss women or men) and gender stereotypical topics (e.g., work or family) in our comments, where we interpret a strong correlation as the presence of gender stereotypes. We find that men are indeed discussed relatively more often in the context of stereotypical male topics such as work and money, and that women are discussed relatively more often in the context of stereotypical female topics such as family, home, and physical appearance. While the prevalence of gender stereotypes related to stereotypical male topics diminishes over time, gender stereotypes related to female topics mostly persist.
Keywords: C55; J16; 330; gender bias; gender stereotypes; natural language processing; machine learning; user-generated content; word embeddings