Analyzing Gender Bias within Narrative Tropes

Dhruvil Gala, Mohammad Omar Khursheed, Hannah Lerner, Brendan O’Connor, Mohit Iyyer


Abstract
Popular media reflects and reinforces societal biases through the use of tropes, which are narrative elements, such as archetypal characters and plot arcs, that occur frequently across media. In this paper, we specifically investigate gender bias within a large collection of tropes. To enable our study, we crawl tvtropes.org, an online user-created repository that contains 30K tropes associated with 1.9M examples of their occurrences across film, television, and literature. We automatically score the “genderedness” of each trope in our TVTROPES dataset, which enables an analysis of (1) highly-gendered topics within tropes, (2) the relationship between gender bias and popular reception, and (3) how the gender of a work’s creator correlates with the types of tropes that they use.
Anthology ID:
2020.nlpcss-1.23
Volume:
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | NLP+CSS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
212–217
Language:
URL:
https://www.aclweb.org/anthology/2020.nlpcss-1.23
DOI:
10.18653/v1/2020.nlpcss-1.23
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.nlpcss-1.23.pdf