The Gender Divide in Wikipedia: A Computational Approach to Assessing the Impact of Two Feminist Interventions

Abstract

Wikipedia has a well-known gender divide affecting the diversity of the editor community and its biographical content. This bias not only shapes social perceptions of knowledge, but it can also propagate beyond the platform as its contents are leveraged to correct misinformation, train machine-learning tools, and enhance search engine results. What happens when feminist movements intervene to try to close existing gaps? Through the analysis of over 11,000 Wikipedia articles, we provide an evaluation of two popular feminist interventions designed to counteract digital knowledge inequality. We find that the interventions are successful at adding content about women that would otherwise be missing, but they are less successful at addressing structural biases that limit the visibility of women focused content. This leads us to argue for a more granular analysis of gender gaps in collaborative environments. We discuss the implications for future scholarship on digital inequalities.