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Analyzing Gender Bias in Narrative Tropes

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.

Paper

Here is the official page for our paper.

Data

We crawled TVTropes.org to collect a large-scale dataset of 30K tropes and 1.9M examples of their occurrences across 40K works of film, television, and literature. We then connected our data to meta-data from IMDb and Goodreads to augment our dataset and enable analysis of gender bias.

Our data can be found here (~650 MB). It contains the following:

• tropes contains trope names, IDs, and descriptions
• lit_tropes, film_tropes, and tv_tropes contain the trope names, titles, and examples across each form of media
• lit_goodreads_match, film_imdb_match, tv_imdb_match contain the tropes, examples, and titles linked to the metadata

Samples of each table are shown at the end of this document.

Code

Each script contains the code for each different analysis conducted in the paper. Please ensure you have the requirements listed in requirements.txt installed to run the scripts.

Citation

If you use this dataset or code for your research, please cite:

@inproceedings{gala-etal-2020-analyzing,
    title = "Analyzing Gender Bias within Narrative Tropes",
    author = "Gala, Dhruvil  and
      Khursheed, Mohammad Omar  and
      Lerner, Hannah  and
      O{'}Connor, Brendan  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.23",
    doi = "10.18653/v1/2020.nlpcss-1.23",
    pages = "212--217",
    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.",
}

Data samples

film_imdb_match

film_tropes

genderedness_filtered

lit_goodreads_match

lit_tropes

tropes

tv_imdb_match

tv_tropes