![]() Our results highlight how and where gender bias manifests in the film industry and provides an automatic way to evaluate it over time. These results strengthen previous studies‘ results that women play fewer central roles (Agarwal et al., 2015 Lauzen, 2018b), and indicates that on average women have more minor roles. Another sign of the underrepresentation of women in movies is found by analyzing interactions among three characters: only 3.57% of the interactions are among three women, while 40.74% are among three men. These differences indicate that men are getting more central roles in movies than women (see Fig. The godfather 2 subtitles online script movie#Our results demonstrate that in most movie genres there is a statistically significant difference between men and women in centrality features like betweenness and closeness. Moreover, by utilizing the dataset, we developed a machine-learning classifier, which is able to assess, how fairly women are represented in movies (i.e., if a movie passes the Bechdel test (Bechdel, 1985)). By analyzing these features, we could observe the gender gap across movie genres and over the last 99 years. Using the constructed movie social networks, we extracted dozens of topological features that characterized each movie. This is the largest study to date that uses social network analysis (SNA) to investigate the gender gap problem in the film industry and how it evolved. We demonstrate possible utilizations of Subs2Network by employing the latest data science tools to comprehensively analyze gender in movies (see Fig. In this study, we present Subs2Network, a novel algorithm to construct a movie character’s social network. Most previous gender studies can be categorized into two types: the first type offers simple statistics from the data to emphasize the gender gap (Lauzen, 2018b) and the second type introduces more advanced analytical methods, yet generally uses only a small amount of data (Agarwal et al., 2015 Garcia et al., 2014). While the gender gap in the film industry is a well-known issue (Lauzen, 2018a Rose, 2018 Cohen, 2017 Lauzen, 2018b Wood, 1994), there is still much value in researching this topic. A recent study found that the underrepresentation is so sizeable that there are twice as many male speaking characters as female in the average movie (Lauzen, 2018a). Studies from the past two decades have confirmed that women in the film industry are both underrepresented (University, 2017 Lauzen, 2018b) and portrayed stereotypically (Wood, 1994). With such a gender bias, it is not surprising that there is a male gender dominance in movies (Smith and Choueiti, 2010 Ramakrishna et al., 2017). It is well known that movie directors are primarily white and male (Smith et al., 2017). Movies are the fulfillment of the vision of the movie director, who controls all aspects of the filming. As just one example, a new study shows that “women who regularly watch The X-Files are more likely to express interest in STEM, major in a STEM field in college, and work in a STEM profession than other women in the sample” (Fox, 2018). In particular, the representation of women in media has an enormous influence on society. Now more than ever, the media has a major influence on our daily lives (Silverstone, 2003), significantly influencing how we think (Entman, 1989), what we wear (Wilson and MacGillivray, 1998), and our self-image (Polce-Lynch et al., 2001). The film industry is one of the strongest branches of the media, reaching billions of viewers worldwide (MPAA, 2018 UNIC, 2017). Our study introduces fresh data, an open-code framework, and novel techniques that present new opportunities in the research and analysis of movies. Here we propose a new and better alternative to this test for evaluating female roles in movies. There has also been an increase in the number of movies that pass the well-known Bechdel test, a popular-albeit flawed-measure of women in fiction. We find a trend of improvement in all aspects of women‘s roles in movies, including a constant rise in the centrality of female characters. Analyzing this data, we investigated gender bias in on-screen female characters over the past century. To this end, we fused data from the online movie database IMDb with a dataset of movie dialogue subtitles to create the largest available corpus of movie social networks (15,540 networks). Here we turn attention to the portrayal of women in movies, an industry that has a significant influence on society, impacting such aspects of life as self-esteem and career choice. Data science can offer answers to a wide range of social science questions. ![]()
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