Natural Language Processing Meets Computational Social Science

Student: Nikolaeva Sofia

Supervisor: Ekaterina Artemova

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2021

Analyzing gender bias through tropes, narrative patterns, is a popular field of studying in social science. In this paper, we explore gender biases using tropes, narrative patterns, in movies and TV series using large datasets from online repositories tvtropes.org, imdb.com and wikidata.org. For each trope in the datasets, we estimate its gender identity with the “genderedness” metric, which allows us to analyze (1) the change in highly-gendered tropes in films and TV shows over time, (2) the relationship between gendered tropes and the budget of the films in which they are used, (3) the relationship between the diversity of highly-gendered tropes and the box office of films, and (4) how the gender of film makers and screenwriters correlates with the types of tropes that they use.

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