Gender Bias in the News: A Scalable Topic Modelling and Visualization Framework

Gender bias in the news is examined through a scalable topic modelling and visualization framework applied to a large corpus of Canadian English-language news. The authors combine named-entity-based gender annotation with topic models to surface which topics quote and mention women versus men, and provide an interactive visualization for exploring the results over time and across outlets.