Inequalities in gender representation and characterization in fictional works are issues that have long been discussed by social scientists. This work addresses these inequalities with two interrelated components. First, it contributes a sentiment and word frequency analysis task focused on gender-specific nouns and pronouns in 15,000 fictional works taken from the online library, Project Gutenberg. This analysis allows for both quantifying and offering further insight on the nature of this disparity in gender representation. Then, the outcomes of the analysis are harnessed to explore novel data visualization formats using computational and studio art techniques. Our results call attention to the need for several identified trends in this data set that require more cross-disciplinary investigation to unpack. Further, our combined approach demonstrate both the need for, and possible methods of, communicating data that warrants social change in an evocative and powerful way.
","abstract_html":"<p>Inequalities in gender representation and characterization in fictional works are issues that have long been discussed by social scientists. This work addresses these inequalities with two interrelated components. First, it contributes a sentiment and word frequency analysis task focused on gender-specific nouns and pronouns in 15,000 fictional works taken from the online library, Project Gutenberg. This analysis allows for both quantifying and offering further insight on the nature of this disparity in gender representation. Then, the outcomes of the analysis are harnessed to explore novel data visualization formats using computational and studio art techniques. Our results call attention to the need for several identified trends in this data set that require more cross-disciplinary investigation to unpack. Further, our combined approach demonstrate both the need for, and possible methods of, communicating data that warrants social change in an evocative and powerful way.</p>","abstract_has_math":false,"creators":["Leto, Alexandria"],"institution":null,"degree_name":"M.S.","degree_level":"Masters Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Scott Leutenegger","Sarah Gjertson","Kerstin Haring"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-01T08:00:00Z","date_published":"2022-01-01T08:00:00Z","updated_at":"2026-07-24T02:02:30Z","subjects":["Computational literature analysis","Data visualization","Natural language processing","Sentiment analysis","Computer Sciences","Data Science","Other Computer Sciences"],"languages":["en"],"rights":["Copyright is held by the author. User is responsible for all copyright compliance.
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"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Humanizing Computational Literature Analysis Through Art-Based Visualizations"]}]}],"canonical_facts":{"dc:contributor":["Scott Leutenegger","Sarah Gjertson","Kerstin Haring"],"dc:creator":["Leto, Alexandria"],"dc:description.abstract":["Inequalities in gender representation and characterization in fictional works are issues that have long been discussed by social scientists. This work addresses these inequalities with two interrelated components. First, it contributes a sentiment and word frequency analysis task focused on gender-specific nouns and pronouns in 15,000 fictional works taken from the online library, Project Gutenberg. This analysis allows for both quantifying and offering further insight on the nature of this disparity in gender representation. Then, the outcomes of the analysis are harnessed to explore novel data visualization formats using computational and studio art techniques. Our results call attention to the need for several identified trends in this data set that require more cross-disciplinary investigation to unpack. Further, our combined approach demonstrate both the need for, and possible methods of, communicating data that warrants social change in an evocative and powerful way.
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