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Analyzing Gender Bias Within Narrative Tropes Analyzing Gender Bias within Narrative Tropes Dhruvil GalaF Mohammad Omar KhursheedF Hannah Lerner Brendan O’Connor Mohit Iyyer University of Massachusetts Amherst fdgala,mkhursheed,hmlerner,brenocon,[email protected] Abstract explicitly gendered trope (as opposed to, for exam- ple, women are wiser), the online tvtropes.org Popular media reflects and reinforces societal repository contains 108 male and only 15 female biases through the use of tropes, which are instances of evil genius across film, TV, and litera- narrative elements, such as archetypal charac- ters and plot arcs, that occur frequently across ture. media. In this paper, we specifically inves- To quantitatively analyze gender bias within tigate gender bias within a large collection tropes, we collect TVTROPES, a large-scale dataset of tropes. To enable our study, we crawl that contains 1.9M examples of 30K tropes in vari- tvtropes.org, an online user-created repos- ous forms of media. We augment our dataset with itory that contains 30K tropes associated with metadata from IMDb (year created, genre, rating of 1.9M examples of their occurrences across the film/show) and Goodreads (author, characters, film, television, and literature. We automati- cally score the “genderedness” of each trope gender of the author), which enable the exploration in our TVTROPES dataset, which enables an of how trope usage differs across contexts. analysis of (1) highly-gendered topics within Using our dataset, we develop a simple method tropes, (2) the relationship between gender based on counting pronouns and gendered terms bias and popular reception, and (3) how the to compute a genderedness score for each trope. gender of a work’s creator correlates with the Our computational analysis of tropes and their gen- types of tropes that they use. deredness reveals the following: • Genre impacts genderedness: Media re- 1 Introduction lated to sports, war, and science fiction rely heavily on male-dominated tropes, while ro- Tropes are commonly-occurring narrative patterns mance, horror, and musicals lean female. within popular media. For example, the evil ge- nius trope occurs widely across literature (Lord • Male-leaning tropes exhibit more topical Voldemort in Harry Potter), film (Hannibal Lecter diversity: Using LDA, we show that male- in The Silence of the Lambs), and television (Ty- leaning tropes exhibit higher topic diversity win Lannister in Game of Thrones). Unfortunately, (e.g., science, religion, money) than female many tropes exhibit gender bias1, either explicitly tropes, which contain fewer distinct topics (of- through stereotypical generalizations in their defini- ten related to sexuality and maternalism). tions, or implicitly through biased representation in • Low-rated movies contain more gendered their usage that exhibits such stereotypes. Movies, tropes: Examining the most informative fea- TV shows, and books with stereotypically gendered tures of a classifier trained to predict IMDb tropes and biased representation reify and reinforce ratings for a given movie reveals that gendered gender stereotypes in society (Rowe, 2011; Gupta, tropes are strong predictors of low ratings. 2008; Leonard, 2006). While evil genius is not an • Female authors use more diverse gendered FAuthors contributed equally. tropes than male authors: Using author gen- 1Our work explores gender bias across two identities: cis- der metadata from Goodreads, we show that gender male and female. The lack of reliable lexicons limits our ability to explore bias across other gender identities, which female authors incorporate a more diverse set should be a priority for future work. of female-leaning tropes into their works. 212 Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pages 212–217 Online, November 20, 2020. c 2020 Association for Computational Linguistics https://doi.org/10.18653/v1/P17 Titles (w/ metadata) Tropes Examples 2.3 Who contributes to TVTROPES? Literature 15,495 (5,208) 27,229 679,618 One limitation of any analysis of social bias on Film 17,019 (8,816) 27,450 751,594 TVTROPES is that the website may not be repre- TV 7,921 (4,192) 27,134 488,632 Total 40,435 (18,216) 29,457 1,919,844 sentative of the true distribution of tropes within media. There is a confounding selection bias—the Table 1: Statistics of TVTROPES. media in TVTROPES is selected by the users who maintain the tvtropes.org resource. To better un- derstand the demographics of contributing users, Our dataset and experiments complement existing we scrape the pages of the 15K contributors, many social science literature that qualitatively explore of which contain unstructured biography sections. gender bias in media (Lauzen, 2019). We publicly We search for biographies that contain tokens re- 2 release TVTROPES to facilitate future research that lated to gender and age, and then we manually computationally analyzes bias in media. extract the reported gender and age for a sample of 256 contributors.4 The median age of these con- 2 Collecting the TVTROPES dataset tributors is 20, while 64% of them are male, 33% female and 3% bi-gender, genderfluid, non-binary, We crawl TVTropes.org to collect a large-scale trans, or agender. We leave exploration of whether dataset of 30K tropes and 1.9M examples of their user-reported gender correlates with properties of occurrences across 40K works of film, television, contributed tropes to future work. and literature. We then connect our data to meta- data from IMDb and Goodreads to augment our 3 Measuring trope genderedness dataset and enable analysis of gender bias. We limit our analysis to male and female genders, 2.1 Collecting a dataset of tropes though we are keenly interested in examining the Each trope on the website contains a description correlations of other genders with trope use. We as well as a set of examples of the trope in differ- devise a simple score for trope genderedness that ent forms of media. Descriptions normally consist relies on matching tokens to male and female lex- 5 of multiple paragraphs (277 tokens on average), icons used in prior work (Bolukbasi et al., 2016; while examples are shorter (63 tokens on average). Zhao et al., 2018) and include gendered pronouns, We only consider titles from film, TV, and litera- possessives (his, her), occupations (actor, actress), ture, excluding other forms of media, such as web and other gendered terms. We validate the effec- comics and video games. We focus on the former tiveness of the lexicon in capturing genderedness because we can pair many titles with their IMDb by annotating 150 random examples of trope occur- and Goodreads metadata. Table1 contains statistics rences as male (86), female (23), or N/A (41). N/A of the TVTROPES dataset. represents examples that do not capture any aspect of gender. We then use the lexicon to classify each 2.2 Augmenting TVTROPES with metadata example as male (precision = 0:85, recall = 0:86, and F1 score = 0:86) or female (precision = 0:72, 3 We attempt to match each film and television listed recall = 0:78, and F1 score = 0:75). in our dataset with publicly-available IMDb meta- To measure genderedness, for each trope i, we data, which includes year of release, genre, director concatenate the trope’s description with all of the and crew members, and average rating. Similarly, trope’s examples to form a document Xi. Next, we match our literature examples with metadata we tokenize, preprocess, and lemmatize Xi using scraped from Goodreads, which includes author NLTK (Loper and Bird, 2002). We then compute names, character lists, and book summaries. We the number of tokens in Xi that match the male lex- additionally manually annotate author gender from icon, m(Xi), and the female lexicon, f(Xi). We Goodreads author pages. The second column of also compute m(TVTROPES) and f(TVTROPES), Table1 shows how many titles were successfully the total number of matches for each gender across matched with metadata through this process. 4We note that some demographics may be more inclined 2http:/github.com/dhruvilgala/tvtropes to report age and gender information than others. 3We match by both the work’s title and its year of release 5The gender-balanced lexicon is obtained from Zhao et al. to avoid duplicates. (2018) and comprises 222 male-female word pairs. 213 g g Male Tropes Female Tropes TV Musical Movies Motivated by Fear -1.8 Ms. Fanservice 3.4 Romance Horror Robot War -1.6 Socialite 3.1 Drama Cure for Cancer -1.5 Damsel in Distress 2.7 Comedy Evil Genius -1.3 Hot Scientist 2.2 Family Thriller Grand Finale -1.2 Ditzy Secretary 2.0 Biography Mystery Crime Table 2: Instances of highly-gendered tropes. Fantasy Genre Animation Sci-Fi Documentary Adventure all trope documents in the corpus. The raw gen- Action deredness score of trope i is the ratio di = Western War History , Music f(Xi) f(TVTROPES) : Sport f(Xi) + m(Xi) f(TVTROPES) + m(TVTROPES) | {z } | {z } 0.4 0.2 0.0 0.2 0.4 0.6 ri rTVTROPES Genderedness Score This score is a trope’s proportion of female tokens Figure 1: Genderedness across film and TV genres. among gendered tokens (ri), normalized by the global ratio in the corpus (rTVTROPES=0.32). If di is high, trope i contains a larger-than-usual proportion extract the set of all tropes used in these works. of female words. Next, we compute the average genderedness score We finally calculate the the genderedness score of all of these tropes. Figure1 shows that media 6 gi as di’s normalized z-score. This results about sports, war, and science fiction contain more in scores from −1:84 (male-dominated) to 4:02 male-dominated tropes, while musicals, horror, and (female-dominated). For our analyses, we consider romance shows are heavily oriented towards female tropes with genderedness scores outside of [−1; 1] tropes, which is corroborated by social science lit- (one standard deviation) to be highly gendered (see erature (Lauzen, 2019).
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