Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies
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Proceedings of Machine Learning Research 81:1{14, 2018 Conference on Fairness, Accountability, and Transparency Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies Nishtha Madaan [email protected] Sameep Mehta [email protected] IBM Research-INDIA Taneea S Agrawaal [email protected] Vrinda Malhotra [email protected] Aditi Aggarwal [email protected] IIIT-Delhi Yatin Gupta [email protected] MSIT Delhi Mayank Saxena [email protected] DTU-Delhi Abstract 1. Introduction Movies are a reflection of the society. They mir- ror (with creative liberties) the problems, issues, The presence of gender stereotypes in many thinking & perception of the contemporary soci- aspects of society is a well-known phe- ety. Therefore, we believe movies could act as nomenon. In this paper, we focus on the proxy to understand how prevalent gender studying such stereotypes and bias in Hindi bias and stereotypes are in any society. In this movie industry (Bollywood) and propose an paper, we leverage NLP and image understand- algorithm to remove these stereotypes from ing techniques to quantitatively study this bias. text. We analyze movie plots and posters To further motivate the problem we pick a small for all movies released since 1970. The section from the plot of a blockbuster movie. gender bias is detected by semantic model- ing of plots at sentence and intra-sentence "Rohit is an aspiring singer who works as a level. Different features like occupation, salesman in a car showroom, run by Malik (Dalip introductions, associated actions and de- Tahil). One day he meets Sonia Saxena (Amee- scriptions are captured to show the perva- sha Patel), daughter of Mr. Saxena (Anupam siveness of gender bias and stereotype in Kher), when he goes to deliver a car to her home movies. Using the derived semantic graph, as her birthday present." we compute centrality of each character This piece of text is taken from the plot of Bol- and observe similar bias there. We also lywood movie Kaho Na Pyaar Hai. This simple show that such bias is not applicable for two line plot showcases the issue in following fash- movie posters where females get equal im- portance even though their character has ion: little or no impact on the movie plot. The 1. Male (Rohit) is portrayed with a profession silver lining is that our system was able to & an aspiration identify 30 movies over last 3 years where 2. Male (Malik) is a business owner such stereotypes were broken. The next In contrast, the female role is introduced with step, is to generate debiased stories. The no profession or aspiration. The introduction, proposed debiasing algorithm extracts gen- der biased graphs from unstructured piece itself, is dependent upon another male character of text in stories from movies and de-bias "daughter of"! these graphs to generate plausible unbiased One goal of our work is to analyze and quantify stories. gender-based stereotypes by studying the demar- c 2018 N. Madaan, S. Mehta, T.S. Agrawaal, V. Malhotra, A. Aggarwal, Y. Gupta & M. Saxena. Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies cation of roles designated to males and females. correspond with the gender stereotypes which ex- We measure this by performing an intra-sentence ist in society? and inter-sentence level analysis of movie plots combined with the cast information. Captur- ing information from sentences helps us perform 2. Related Work a holistic study of the corpus. Also, it helps While there are recent works where gender bias us in capturing the characteristics exhibited by has been studied in different walks of life Soklar- male and female class. We have extracted movies idis et al.(2017),(MacNell et al., 2015), (Carnes pages of all the Hindi movies released from 1970- et al., 2015), (Terrell et al., 2017), (Saji, 2016), present from Wikipedia. We also employ deep the analysis majorly involves information re- image analytics to capture such bias in movie trieval tasks involving a wide variety of prior posters and previews. work in this area. (Fast et al., 2016) have worked on gender stereotypes in English fiction particu- 1.1. Analysis Tasks larly on the Online Fiction Writing Community. The work deals primarily with the analysis of We focus on following tasks to study gender bias how males and females behave and are described in Bollywood. in this online fiction. Furthermore, this work I) Occupations and Gender Stereotypes- also presents that males are over-represented and How are males portrayed in their jobs vs females? finds that traditional gender stereotypes are com- How are these levels different? How does it cor- mon throughout every genre in the online fiction relate to gender bias and stereotype? data used for analysis. II) Appearance and Description - How are Apart from this, various works where Hollywood males and females described on the basis of their movies have been analyzed for having such gen- appearance? How do the descriptions differ in der bias present in them (Anderson and Daniels, both of them? How does that indicate gender 2017). Similar analysis has been done on chil- stereotyping? dren books (Gooden and Gooden, 2001) and mu- III) Centrality of Male and Female Char- sic lyrics (Millar, 2008) which found that men acters - What is the role of males and females are portrayed as strong and violent, and on the in movie plots? How does the amount of male other hand, women are associated with home and being central or female being central differ? How are considered to be gentle and less active com- does it present a male or female bias? pared to men. These studies have been very IV) Mentions(Image vs Plot) - How many useful to uncover the trend but the derivation males and females are the faces of the promo- of these analyses has been done on very small tional posters? How does this correlate to them data sets. In some works, gender drives the de- being mentioned in the plot? What results are cision for being hired in corporate organizations conveyed on the combined analysis? (Dobbin and Jung, 2012). Not just hiring, it has V) Dialogues - How do the number of dia- been shown that human resource professionals' logues differ between a male cast and a female decisions on whether an employee should get a cast in official movie script? raise have also been driven by gender stereotypes VI) Singers - Does the same bias occur in by putting down female claims of raise requests. movie songs? How does the distribution of While, when it comes to consideration of opinion, singers with gender vary over a period of time views of females are weighted less as compared for different movies? to those of men (Otterbacher, 2015). On social VII) Female-centric Movies- Are the movie media and dating sites, women are judged by stories and portrayal of females evolving? Have their appearance while men are judged mostly by we seen female-centric movies in the recent past? how they behave (Rose et al., 2012; Otterbacher, VIII) Screen Time - Which gender, if any, 2015; Fiore et al., 2008). When considering oc- has a greater screen time in movie trailers? cupation, females are often designated lower level IX) Emotions of Males and Females - roles as compared to their male counterparts in Which emotions are most commonly displayed by image search results of occupations (Kay et al., males and females in a movie trailer? Does this 2015). In our work we extend these analyses for 2 Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies Bollywood movies. trailers were obtained from YouTube. The mean The motivation for considering Bollywood movies and standard deviation of the duration of the all is three fold: videos is 146 and 35 seconds respectively. The a) The data is very diverse in nature. Hence videos have a frame rate of 25 FPS and a reso- finding how gender stereotypes exist in this data lution of 480p. Each 25th frame of the video is becomes an interesting study. extracted and analyzed using face classification b) The data-set is large. We analyze 4000 for gender and emotion detection (Octavio Ar- movies which cover all the movies since 1970. So riaga, 2017). it becomes a good first step to develop compu- tational tools to analyze the existence of stereo- 3.2. Task and Approach types over a period of time. c) These movies are a reflection of society. It In this section, we discuss the tasks we perform is a good first step to look for such gender bias on the movie data extracted from Wikipedia and in this data so that necessary steps can be taken the scripts. Further, we define the approach we to remove these biases. adopt to perform individual tasks and then study the inferences. At a broad level, we divide our analysis in four groups. These can be categorized 3. Data and Experimental Study as follows- a) At intra-sentence level - We perform this 3.1. Data Selection analysis at a sentence level where each sentence We deal with (three) different types of data for is analyzed independently. We do not consider Bollywood Movies to perform the analysis tasks- context in this analysis. b) At inter-sentence level - We perform this 3.1.1. Movies Data analysis at a multi-sentence level where we carry context from a sentence to other and then analyze Our data-set consist of all Hindi movie pages the complete information. from Wikipedia. The data-set contains 4000 c) Image and Plot Mentions - We perform this movies for 1970-2017 time period. We extract analysis by correlating presence of genders in movie title, cast information, plot, soundtrack in- movie posters and in plot mentions.