Computational linguistic lens into gendered actions in film.

Our framework starts with a dataset of over 1.2 million movie descriptors from 912 movies and then implements three steps: first, the annotation process, where we collect manual annotations for 9,613 descriptions and over 1.5 million gender expression labels for characters. In the second step, we develop a machine learning model to identify actions, agents and patients from the natural language found in the movie scripts. Our model—trained on a large-set of general-domain documents and fine-tuned on a manually labeled description set—identifies actions, agents and patients from the natural language descriptions found in movie scripts. We use this model to automatically label the complete dataset for analysis. In the final third step, we perform a series of statistical analysis to uncover portrayal differences along characters’ portrayed attributes.