Analyzing Gender Bias within Narrative Tropes (2020)

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.

## introduction tropes are commonly-occurring narrative patterns within popular media. for example, the evil genius trope occurs widely across literature (lord voldemort in harry potter), film (hannibal lecter in the silence of the lambs), and television (tywin lannister in game of thrones). unfortunately, many tropes exhibit gender bias foot_0 , either explicitly through stereotypical generalizations in their definitions, or implicitly through biased representation in their usage that exhibits such stereotypes. movies, tv shows, and books with stereotypically gendered tropes and biased representation reify and reinforce gender stereotypes in society @xcite @xcite . while evil genius is not an explicitly gendered trope (as opposed to, for example, women are wiser), the online tvtropes.org repository contains 108 male and only 15 female instances of evil genius across film, tv, and literature. to quantitatively analyze gender bias within tropes, we collect tvtropes, a large-scale dataset that contains 1.9m examples of 30k tropes in various forms of media. we augment our dataset with metadata from imdb (year created, genre, rating of the film/show) and goodreads (author, characters, gender of the author), which enable the exploration of how trope usage differs across contexts. using our dataset, we develop a simple method based on counting pronouns and gendered terms to compute a genderedness score for each trope. our computational analysis of tropes and their genderedness reveals the following: ## collecting the tvtropes dataset we crawl 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 connect our data to metadata from imdb and goodreads to augment our dataset and enable analysis of gender bias. ## measuring trope genderedness we limit our analysis to male and female genders, though we are keenly interested in examining the correlations of other genders with trope use. we devise a simple score for trope genderedness that relies on matching tokens to male and female lexicons foot_4 used in prior work @xcite and include gendered pronouns, possessives (his, her), occupations (actor, actress), and other gendered terms. we validate the effectiveness of the lexicon in capturing genderedness by annotating 150 random examples of trope occurrences as male (86), female (23), or n/a (41). n/a represents examples that do not capture any aspect of gender. we then use the lexicon to classify each example as male (@xmath0.85, @xmath1.86, and f1 @xmath2.86) or female (@xmath3.72, @xmath4.78, and f1 @xmath5.75). to measure genderedness, for each trope i, we concatenate the trope's description with all of the trope's examples to form a document x i . next, we tokenize, preprocess, and lemmatize x i using nltk @xcite . we then compute the number of tokens in x i that match the male lexicon, m(x i ), and the female lexicon, f (x i ). we also compute m(tvtropes) and f (tvtropes), the total number of matches for each gender across all trope documents in the corpus. the raw genderedness score of trope i is the ratio this score is a trope's proportion of female tokens among gendered tokens (r i ), normalized by the global ratio in the corpus (r @xmath6.32). if d i is high, trope i contains a larger-than-usual proportion of female words. we finally calculate the the genderedness score g i as d i 's normalized z-score. 6 this results in scores from -1.84 (male-dominated) to 4.02 (female-dominated). for our analyses, we consider tropes with genderedness scores outside of [-1, 1] (one standard deviation) to be highly gendered (see while similar to methods used in prior work @xcite , our genderedness score is limited by its lexicon and susceptible to gender generalization and explicit marking @xcite . we leave exploration of more nuanced methods of capturing trope genderedness @xcite to future work. ## analyzing gender bias in tvtropes having collected tvtropes and linked each trope with metadata and genderedness scores, we now turn to characterizing how gender bias manifests itself in the data. we explore (1) the effects of genre on genderedness, (2) what kinds of topics are used in highly-gendered tropes, (3) what tropes contribute most to imdb ratings, and (4) what types of tropes are used more commonly by authors of one gender than another. ## future work we believe that the tvtropes dataset can be used to further research in a variety of areas. we envision setting up a task involving trope detection from raw movie scripts or books; the resulting classifier, beyond being useful for analysis, could also be used by media creators to foster better representation during the writing process. there is also the possibility of using the large number of examples we collect in order to generate augmented training data or adversarial data for tasks such as coreference resolution in a gendered context @xcite . the expansion of our genderedness metric to include non-binary gender idenities, which in turn would involve creating similar lexicons as we use, is an important area for further exploration. it would also be useful to gain further understanding of the multiple online communities that contribute information about popular culture; for example, an analysis of possible overlap in contributors to tvtropes and imdb could better account for sampling bias when analyzing these datasets.