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Gender Stereotypes in Modern Movies: Beauty and The Beast

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Works Cited

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• Galambos, N. L., Berenbaum, S. A., & McHale, S. M. (2011). Gender development in adolescence. In I. B. Weiner & W. E. Craighead (Eds.), The Corsini Encyclopedia of Psychology (4th ed., Vol. 2, pp. 701-704). Hoboken, NJ: Wiley.
• Goodenow, C. (1993). Classroom belonging among early adolescent students: Relationships to motivation and achievement. Journal of Early Adolescence, 13(1), 21-43.
• Hyde, J. S. (2014). Gender similarities and differences. Annual Review of Psychology, 65, 373-398.
• Martin, C. L., & Ruble, D. N. (2010). Patterns of gender development. Annual Review of Psychology, 61, 353-381.
• Schmitt, D. P. (2015). The evolution of culturally-variable sex differences: Men and women are not always different, but when they are... it appears not to result from patriarchy or sex role socialization. In D. P. Schmitt (Ed.), The Oxford Handbook of Evolutionary Psychology and Behavioral Endocrinology (pp. 261-280). Oxford University Press.
• Signorella, M. L., Bigler, R. S., & Liben, L. S. (2018). Developmental approaches to stereotype and prejudice reduction. In S. R. Levy & M. Killen (Eds.), Intergroup Attitudes and Relations in Childhood through Adulthood (pp. 91-113). Oxford University Press.
• Skalnik, P., & Petersen, J. L. (2017). Gender socialization: Beliefs, practices, and challenges. In T. D. Fisher & C. M. Davis (Eds.), Handbook of Gender, Sex, and Media (pp. 11-30). Wiley-Blackwell.
• Wood, W., & Eagly, A. H. (2015). Two traditions of research on gender identity. Sex Roles, 73(11-12), 461-473.

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• Published: 10 March 2023

Identifying gender bias in blockbuster movies through the lens of machine learning

• Muhammad Junaid Haris 1   na1 ,
• Aanchal Upreti 1   na1 ,
• Melih Kurtaran 1 ,
• Filip Ginter 2 ,
• Sebastien Lafond 1 &
• Sepinoud Azimi 1  

Humanities and Social Sciences Communications volume  10 , Article number:  94 ( 2023 ) Cite this article

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• Cultural and media studies
• Science, technology and society

The problem of gender bias is highly prevalent and well known. In this paper, we have analysed the portrayal of gender roles in English movies, a medium that effectively influences society in shaping people’s beliefs and opinions. First, we gathered scripts of films from different genres and derived sentiments and emotions using natural language processing techniques. Afterwards, we converted the scripts into embeddings, i.e., a way of representing text in the form of vectors. With a thorough investigation, we found specific patterns in male and female characters’ personality traits in movies that align with societal stereotypes. Furthermore, we used mathematical and machine learning techniques and found some biases wherein men are shown to be more dominant and envious than women, whereas women have more joyful roles in movies. In our work, we introduce, to the best of our knowledge, a novel technique to convert dialogues into an array of emotions by combining it with Plutchik’s wheel of emotions. Our study aims to encourage reflections on gender equality in the domain of film and facilitate other researchers in analysing movies automatically instead of using manual approaches.

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Introduction.

As the movie industry is one of the influential branches of the media reaching billions of viewers worldwide, they play a great role in shaping the beliefs and opinions of people (Sparks, 2015 ). Since blockbuster films are those that make money by giving the audience what they want, like, or expect, thus the portrayal of gender roles in blockbuster movies has a direct influence on viewers, possibly reinforcing or contributing to societal gender stereotypes (Ward and Grower, 2020 ). Thus, in our work, we aim to study and analyse gender bias based on emotions expressed by male and female characters in movies using different Natural Language Processing (NLP) techniques. For this purpose, we chose around thirty blockbuster English movies from IMDb (IMDb, 2019 ) and performed a comparative analysis of the characters. The study is mainly based on the statistical distribution of male and female characters over a certain period and the sentiment and emotions expressed in their dialogues. Through our work, we are trying to present an approach to understanding and promoting studies on the highly prevalent issue of gender inequality in movies. Furthermore, our analysis allows us to help address the global problem of gender inequality and bring positive social change using NLP.

Related works

Several studies have emerged in the past two decades that attempt to assess the gender gap between men and women in various fields (Lariviére et al., 2013 ; Lauzen, 2019 ; Wagner et al., 2015 ).

Many studies have utilised the Bechdel Test (Bechdel, 1986 ) to evaluate gender bias in movies. Kagan et al. (Kagan et al., 2020 ) in their studies presented 15,540 movie social networks where their findings showed a gender gap in almost all genres of the film industry. However, they also found that all aspects of women’s roles in movies show a trend of improvement over the years with a constant rise in the centrality of female characters and the number of movies that pass the Bechdel test.

Xu et al. (Xu et al., 2019 ) in their analysis of 7226 books, 6087 film synopsis, and 1109 film scripts, identified the constructed emotional dependence of female characters on male characters, also called the Cinderella complex, where women depend on men for a happy and fulfilling life. They used the word embedding techniques with word vectors trained on the Google News dataset to construct a happiness vector for automatically calculating the happiness score for every word in the document. The happiness vector contains words such as success, succeed, luck, fortune, happy, glad, joy, and smile for positive and failure, fail, unfortunate, unhappy, sad, sorrow, and tear for negative sentiment. Their analysis of female and male characters showed that female’s word vector is oriented toward romance, whereas men’s toward adventure. Their studies showed how such narratives embed stereotypical gender roles into moral tales and institutionalise gender inequality through these cultural products.

Similarly, Yu et al. (Yu et al., 2017 ) analysed the emotion represented in Korean thriller movie scripts. The authors performed manual emotion annotation on movie scenes based on eight emotion types defined by Plutchik (Plutchik, 1988 ). Further, they used the Python-based NLTK VADERSentiment tool to analyse the sentiment of the same script and compared the results obtained with manual tagging. Their result showed that the emotions of anger and fear were most matched, whereas the emotion of surprise, anticipation, and disgust had a lower matching score.

Anikina (Anikina, 2017 ) in her work performed manual annotation on movie dialogues based on Plutchik’s emotion wheel. The author used machine learning classifiers (fastText, OpenNLP and SVM) to train on different datasets and implemented rule-based NRC emotion lexicon classifiers to detect the emotions. The study compared the results obtained from NRC Lexicon with manually annotated data to find the accuracy. The study highlighted how the unavailability of annotated resources on movie script domain affected the training process as the data from different domains had to be used and also discussed the limitations of used classifiers in performing multi-label emotion classification.

Similarly, the Bechdel test mostly focuses on assessing the fairness of female representation by three rules, which fails even for female oriented movies (Coyle, 2018 ; Florio, 2019 ; Waletzko, 2015 ) and also does not address stereotypes. In this study, we address the limitations of the Bechdel test by expanding our work to examine the stereotypes by employing both sentiment analysis and the analysis of the embedded emotions. Moreover, in previous studies, stereotypes are generally only analysed using word embedding techniques by creating word vectors. In our work, we are not only relying on the word vectors but also analysing the sentiment and emotions embedded with those words for better representation of our results and hence expanding the work beyond just the positive-negative affect dimension. Further, we present a novel approach to converting dialogues into an array of emotions by combining it with Plutchik’s wheel of emotions.

In this section, we present the workflow of our approaches, as presented in Fig. 1 . A brief description of the three modules is as follows. More detailed information on each module is provided in sections ‘Data processing module, Emotion recognition module, Analysis module’, respectively.

(1) data processing module, (2) emotion detection module, and (3) analysis module.

Data processing module

In this module, we convert the scripts of movies from PDF into a machine-readable format. Movie scripts are first converted from PDF into HTML using online tools. Then we process the HTML and separate scenarios and dialogues of each character.

Emotion recognition module

This module looks at every character’s prevalent positive and negative sentiments using Stanza (Qi et al., 2020 ), a Python natural language analysis package. Next, we run NRCLex (Mohammad and Turney, 2013 )—a rule-based model for emotion detection trained on 27,000 manually annotated common words and phrases. From this step, we get the “emotion scores" for eight primary emotions: fear, anger, trust, surprise, sadness, disgust, joy, and anticipation. Finally, with the help of Plutchik’s wheel of emotions, which describes how emotions are related, we compute 24 secondary emotions. In this module, we convert sentences into embeddings. An embedding is a learned numerical representation of sentences in the form of a vector, where similar sentences will have a similar representation.

Analysis module

In the last module, we perform empirical analysis, statistical methods such as Mann–Whitney U -test, and machine learning techniques, namely clustering and classification, to determine if gender bias exists in the collection of scenarios. The way we determine if bias exists is explained in each section, For example, after running the Mann–Whitney U -test, if we get different U -scores for female and male populations with a significant P -value, we could be certain that they are written differently. Our approach is modular, and modules are loosely coupled and highly cohesive.

In this section, we present the first module of this study, i.e., the data processing module. This module includes data collection and data processing steps.

Data collection

To ensure the global reach of the movies and their impact on society, we opted only for the top-rated movies of all time listed on IMDB. We downloaded scripts of 34 movies from different genres, namely romance, fantasy, fiction, drama, and action, spread over the years ranging from 1972 to 2021. We considered several parameters while choosing the scripts: the accessibility of the scripts, the compatibility of the script’s format with our data processing module, and overall how popular the movie was. Initially, we started with a list of the top 1000 popular movies of all time on IMDB and grouped them into five chunks. We then randomly selected ten movies from each chunk. In the case of series such as Harry Potter, all of the movies were kept. Afterwards, we dropped the movies whose script was not freely available, or the quality of the available manuscript was not suitable to be processed by our module. The final dataset was capped at 34. The year distribution of the movies is presented in Fig. 2 . In total, we collected 26,279 dialogues and 457 characters, out of which 118 are female characters, and 339 are male.

Bar graph showing the distribution of the selected movies based on their published year.

Data processing

Movie scripts are technical documents created by screenwriters and serve as an essential reference for filmmakers. Blocks of text divided into scene headers, scene descriptions, speakers, and utterances are parts of a standard movie script. The critical point is the different indentations used for every block. The typical format of scripts is shown in Fig. 3 .

The way movie scripts are typically composed.

We converted these scripts into a processed set of scenarios and dialogues where individual characters’ information is extracted and exploited using the information about indentation. We would refer to them as character dictionaries for the rest of this paper. We first converted PDF files into HTML format and used BeautifulSoup (Richardson, 2007 )—a Python library for pulling data out of HTML—and regex to create character dictionaries. In the HTML files, the style attribute of every element has a property named ‘left’ and ‘top’, which indicates the value of the starting pixel from the left, i.e., indentation and dialogue sequence, respectively. We have stored the processed version of scripts in JSON format, i.e., attribute-value pairs, where the attribute is the name of the character and the value is the list of dialogues.

In the next step, we created a database from the character dictionaries by further processing the data. Screenwriters sometimes add extra text to the character’s name, such as (V.O.), (O.S.), etc., suggesting a voice-over or off-screen dialogue. First, we processed such terms and deleted this extra information because it is irrelevant to our study. Then, we tagged each character with their respective genders.

Furthermore, we added the movie’s name and the year it was released into the database for visualisation and analysis purposes. Finally, we dropped the characters with less than five dialogues since they did not contribute to our results in a meaningful way. Performing all these steps, we generated a final database, which was also manually validated to ensure its correctness.

In this section, we present the second module of this study, i.e., the emotion recognition module. This module includes finding sentiments and emotions for each character.

Sentiment analysis

We first used the Stanza package (Qi et al., 2020 ) to find the sentiment of every dialogue. Stanza is a Python natural language analysis package that supports sentiment analysis on raw text from diverse sources as input. It produces annotations without having to annotate or tokenize the text manually. It is built with a highly accurate CNN classifier pipeline trained on 112 datasets, enabling efficient training and evaluation of annotated data. The reason for using Stanza is that it is built on top of the PyTorch library (Paszke et al., 2019 ), which gives a high-speed performance on GPU-enabled machines and has better accuracy than other under-optimised models. Stanza classified each dialogue as positive, negative, or neutral. However, more than 70% of the conversations organised by Stanza were neutral. Therefore, Stanza did not provide much information about the bias in male or female characters.

These results prompted us to check the validity of the Stanza model, for which we used the trivial approach of manually annotating the data ourselves and then comparing the result with the Stanza model. To reduce the bias, the same data were manually annotated by two different people, and 179 dialogues spoken by Harry in the Harry Potter series were chosen randomly. After comparing the accuracy of the model with human evaluation, we acquire the following accuracy scores shown in Table 1 .

Emotion detection

By looking at the results of Stanza, we came to know that the overall outlook of positivity and negativity was not drastically different. But, generally, the emotions conveyed by humans are much more complex than only positive and negative. Therefore, for deeper analysis, we ran NRCLex on every dialogue. NRCLex takes a string, i.e., dialogue, as input and returns the emotion scores for the primary eight emotions. Those primary emotions can also be grouped into positive and negative, as suggested by Robert Plutchik. The model classifies joy, anticipation, trust, and surprise as positive emotions and anger, fear, sadness, and disgust as negative emotions. Comparison of genders based on positive vs. negative emotions represented in Fig. 4 .

Bar graph showing the distribution of positive and negative emotion scores obtained after sentiment analysis.

By running NRCLex, we converted every dialogue into an embedding of eight dimensions. Each dimension represents one primary emotion, and its value is a floating-point number showing the likelihood of that sentence conveying that emotion. A sentence could be conveying multiple emotions with varying intensities (Ward and Grower, 2020 ). It’s a probability distribution. Therefore, the embedding or vector sums up to one.

Furthermore, we analysed these primary emotions based on Plutchik’s emotion wheel as shown in Fig. 5 . Psychologist Robert Plutchik proposes it through his famous “wheel of emotions” (Mohsin and Beltiukov, 2019 ). The model shows the intensities of different emotions and how they are interconnected. We use this model as a reference to create complex emotions.

Wheel of emotion showing the interconnection between different emotions.

We computed 24 secondary emotions by averaging the scores of two primary emotions, e.g., envy was derived by taking an average score of sadness and anger. Lastly, we augmented this new information with the previous database and converted every dialogue into a vector of dimension 32. Since there are eight primary emotions and 24 secondary emotions based on Plutchik’s wheel of emotion (Mohsin and Beltiukov, 2019 ). The list of emotions are; anger, joy, anticipation, surprise, trust, delight, sadness, disgust, hope, curiosity, despair, confined, envy, cynicism, pride, love, submission, shame, awe, disapproval, remorse, aggression, anxiety, outrage, fear, dominance, guilt, sentimentality, optimism, pessimism, contempt, and morbidness.

In this section, we present the third module of this study, i.e., the analysis module. This module describes major trends in the data and how characters are written.

Clustering is the process of splitting a population or set of data points into many groups so that data points in the same group are more similar than data points in other groups (Thomas, 2022 ). To put it another way, the goal is to separate groups with similar characteristics and assign them to clusters.

In our final database, we averaged the emotion scores of characters based on all of their dialogues. This new database has a size of 457 x 32, where 457 is the total number of characters. In our database, the ratio of male and female characters is roughly 3:1, i.e., for every female character, there are three male characters. The idea behind applying clustering is that if there is no bias in how male and female characters are written, the clusters made should also follow more or less the same ratio as there is in the data.

We use both k -means clustering and hierarchical clustering. First of all, we used the elbow method on the sum of squared distances to find the optimal number of clusters, which turned out to be seven. For Agglomerative clustering, we use euclidean distance and ward’s linkage, a method for hierarchical clustering. The results of clustering are discussed in section ‘Clustering’.

This section discusses the findings of our analysis using Empirical, Machine Learning, and statistical techniques.

Gender distribution over the years

We have compared the ratio of male and female distribution over the years based on the number of their presence and based on the number of their dialogues. Figure 6 shows grouped total number of characters over the last 20 years. During the years 2000–2004, only 15.1% of characters were females however, during 2015–2019, this percentage increased to 43.9%. It is observed that with the evolution of time, gender distribution is getting more or less balanced.

Gender distribution over the years based on the number of dialogues and the number of their presence in movies.

Mann–Whitney U -test

Based on NRCLex, we have extracted the emotions expressed by male and female characters from their dialogues in all the movies. The obtained emotion score was within the range of 0-1. For detailed analysis, we performed the Mann–Whitney U -test—a form of inferential statistic used to see if there is a significant difference between two groups’ means—on all 32 emotions in the two gender groups. We choose the Mann–Whitney U -test because it is non-parametric and robust against non-normality, and our data is not normal. Through this comparison, we found some notable differences in the distribution of some emotions. The analysis showed that male and female characters exhibit most emotions like anger, aggressiveness, despair, envy, outrage, and love very differently from one another. Male characters have higher values for emotions like anger and aggressiveness. On the other hand, female characters have higher values for emotions such as joy. These results are in line with the gender stereotypes in our society. Women are perceived to be more loving and caring, whereas men are supposed to be more aggressive and powerful.

The U1, U2, and P -value scores of some emotions are shown in Table 2 .

Visualising data

Box plots comparison of female vs. male on average emotion scores is shown in Fig. 7 . Female plots are shown in blue colour, and the male plot is shown in red colour. According to our results, it is observed that male characters have higher scores on the emotions such as aggressiveness and dominance, and female characters have higher scores on emotions such as joy.

a Aggressiveness, b Anger, c Anxiety, d Dominance.

We have used the t-SNE dimensionality reduction technique to visualise the characters’ data. It calculates a similarity measure based on the distance between points and maintains the global structure. In other words, it converts some N-dimensional data into k-dimensional data, where k  <  N and x 1 ,  x 2 , …,  x K are the new axes for the data. In the visualisation shown in Fig. 8 , male characters are represented as blue and female characters as red. It is observed that most females are crowded at the centre, whereas the males are clustered sparsely. This uneven distribution shows a pattern in which characters are primarily written in movies. With the exception of two, most female characters exhibit more or less the same emotions, implying they are written in the same way. In contrast, male characters in the film exhibit a range of emotions and have much more diverse personality traits showing a difference in how they are written.

Gender-wise clustering of characters based on their emotions.

Dialogues analysis

In order to analyse the variations in the dialogues spoken by male and female characters, we performed a sentence-level analysis, which involved extracting the most commonly used words by both genders.

We plotted a word cloud of the words spoken by male and female characters. A word cloud is a graphic depiction of a text’s most commonly occurring terms. Each word’s size in a word cloud reflects how frequently it appears in the text. The word cloud will give us insight into the common themes of words the characters use more often. In Fig. 9 , the word clouds, matching nouns used by male and female characters were excluded in order to trace the unique differences between each gender. In this illustration, we notice that the female characters’ dialogues commonly included the nouns: kitchen, fashion, dress, skirt, sweetheart, and madam; meanwhile, the male characters used the nouns: time, business, war, world, man, and home. These results imply that female characters are shown to be more inclined towards their clothes and household chores. This is also in line with other studies on gender roles (Collins, 2011 ). In comparison, male characters are shown as more ambitious and concerned with business and the world. These results correlate with social stereotypes of gender (Yu et al., 2022 ) in our society.

a Female, b Male.

For further analysis to find out if there is an implicit bias between the traits of male and female characters, we have clustered characters together into seven smaller groups. The clusters are formed based on the similarity between characters, and we can safely say that the groups represent the type or nature of the characters in general. Therefore, characters with similar traits will be grouped and of somewhat the same category. The overall ratio of male and female characters in our database is 3:1. And if there were no bias in how the characters are written, the proportion of male and female characters would be similar in the clusters. However, we employed two different techniques to cluster them, and in both of them, there was an uneven ratio of genders in the smaller groups, as shown in Fig. 10 . Some groups even have a balance of around 6:1, which implies high bias in writing those kinds of characters.

a Hierarchical clustering, b Clustering with k -means.

Pop culture plays a significant role in shaping societal perceptions and attitudes towards gender roles. Through various forms of media such as television, film, music, and social media, pop culture presents images and storylines that can reinforce or challenge traditional gender roles. On the other hand, pop culture can also challenge these stereotypes by featuring strong and complex female characters or depicting men in traditionally feminine roles. Pop culture can also influence how individuals understand and perform their own gender, by providing examples of gender expression and behaviours.

In this study, we have analysed gender representation in a collection of movies and how gender distribution changed over time. Compared to previous works, we rely not only on word vectors but create new embeddings by analysing the emotions associated with these words. We looked into how stereotypes are presented via emotions, making the results more intuitive. Furthermore, we highlighted the hidden bias by using clustering and other ML techniques. The result of this study suggests that although over years the women portrayal have greatly improved, underneath the surface, implicit biases exist. Our clustering results indicate that while the male characters enjoy a variety of emotions when they are portrayed, women are clustered together following the same emotional pattern.

This insight is strengthened by our sentiment analysis of the characters. We found that different kinds of positive emotions are associated with different genders. Those emotions reflect the stereotypes that exist in our society. For example, men are shown to be more dominant and envious than women, and women are shown to be more optimistic and joyful.

The result also indicates that the script writers and the movie producers should be aware of these available biases. There should be a conscious effort to question the portrayal of the characters to make sure the individuality of the characters, independent of their gender, is respected.

We believe that the implicit gender biases are not only available in the movie industry but in a plethora of other cultural products. As a future research direction, it would be interesting to investigate other areas, for example music and conceptual art industry.

Data availability

The datasets generated during and/or analysed during the current study are available in the following github repository: https://github.com/melihkurtaran/DIE_Gender_Equality_in_Movies .

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Acknowledgements

The work has been partially supported by the EMJMD master’s programme in Engineering of Data-Intensive Intelligent Software Systems (EDISS—European Union’s Education, Audiovisual and Culture Executive Agency grant number 619819).

Author information

These authors contributed equally: Muhammad Junaid Haris, Aanchal Upreti.

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Faculty of Science and Engineering, Åbo Akademi University, Turku, 20500, Finland

Muhammad Junaid Haris, Aanchal Upreti, Melih Kurtaran, Sebastien Lafond & Sepinoud Azimi

Department of Computing, University of Turku, Turku, 20500, Finland

Filip Ginter

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Haris, M.J., Upreti, A., Kurtaran, M. et al. Identifying gender bias in blockbuster movies through the lens of machine learning. Humanit Soc Sci Commun 10 , 94 (2023). https://doi.org/10.1057/s41599-023-01576-3

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The New York Times

Movies | what the movies taught me about being a woman, what the movies taught me about being a woman.

By MANOHLA DARGIS NOV. 30, 2018

One of the most ravishing kisses in movies is in “The Quiet Man,” a John Ford classic. Maureen O’Hara plays an Irish villager who falls for John Wayne’s Irish-American stranger. They first see each other while she’s tending sheep barefoot, and initially, they mostly trade searching looks. But one night he finds that this willful woman has sneaked into his house. She runs for the door. He pulls her to him. They scuffle and, as he holds her right arm behind her back, her left arm goes limp. He leans down to kiss her, enfolding her. It’s exquisite; some might call it rapey.

I was a movie-struck kid, and I learned much from watching the screen, including things about men and women that I later had to unlearn or learn to ignore. I learned that women needed to be protected, controlled and left at home. I learned that men led, women followed. And so, although I loved Fred Astaire, I merely liked his greatest dance partner, Ginger Rogers. I was charmed by her sly smile and dazzled by the curve of her waist as she bent in his embrace. But I saw her as a woman in the great man’s arms, a message I didn’t learn just from films.

In the first film book I owned, “The Fred Astaire & Ginger Rogers Book,” the critic Arlene Croce wrote of an Astaire-Rogers number: “The way she gazes up wordlessly at this marvelous man she’s been dancing with exalts him, her, and everything we’ve just seen.” Croce promised me, “Only in Astaire musicals do we dream like this.” The dream metaphor is seductive unless you remember what women are often told to dream. In the wake of Harvey Weinstein and #MeToo, I have been thinking a lot about what movies have asked me to dream, including the image of the forced kiss and all that it signifies about women and film. I’ve been thinking about what else I learned from them.

What Readers Learned at the Movies

We asked readers what the big screen has taught them about being a woman. Here’s what they learned from …

Legally Blonde (2001)

It’s O.K. to be vocal if you’re a woman. There is no need to feel small, unimportant or incapable just because other people in your life make you feel that way.

CLEO PAPADOPOULOS, New York City

Grease (1978)

Good girls are boring. Naughty girls have power.

ERIN COURTENAY, Madison, Wis.

Star Wars (1977)

Women — even young women — could be leaders. Princess Leia was badass. Practically everything she does in the movie could have been done by a man.

JAN COMBOPIANO, Brooklyn

Norma Rae (1979)

Women can be courageous even when frightened.

SUSAN DAVIS, Fort Smith, Ark.

Gone With the Wind (1939)

It taught me that white dominant society saw black women as primarily caretakers, in every sense of the word.

DONNA BAILEY, New York

Alien (1979)

I’d never seen a woman being so fearless and powerful. Whenever I find myself in a stressful situation, I really do think, How would Ripley deal with this?

GABRIELLE ZUCKER, San Francisco

Mulan (1998)

What stuck with me was how she embraces her femininity and uses that as her power, without having to, quite literally, pretend to be a man.

SHALINI CHUDASAMA, Atlanta

She’s All That (1999)

Women make good partners if they’re conventionally “hot.” As a teenage girl, this message really got into my head.

SARA SOUSA, Portugal

This brings me to Mae West, whose voice I could passably imitate by the time I was 10. I didn’t get her double-entendres and was too young to understand camp. For me, West was beautiful, funny, mouthy and generously padded (which I read as plump, like me). She had the best lines and Cary Grant’s adoration, and her sass and saunter compelled everyone to follow her lead. Only as an adult did I learn that she had negotiated great creative control in her films and that her depiction of female sexuality made her a target, including of Hollywood censors. It put her sass into perspective; it also felt like vindication for a mouthy girl.

Movies teach us all sorts of things: how to aspire, who to fantasize about (all those princes will come), how to smoke, dress, walk into a room ( always like Bette Davis). They teach us who to love and how, as well as the ostensible necessity of sacrificing love along with careers. They also teach us that showering, babysitting, being in underground parking lots or simply being female might get you killed. There isn’t a causal relationship between viewer behavior and the screen. There doesn’t have to be. Because movies get into our bodies, making us howl and weep, while their narrative and visual patterns, their ideas and ideologies leave their imprint.

Lesson 1: Women Are There to Be Kissed

The relationship between women and cinema has always been particularly fraught and not just because it often involves what is called the male gaze. Early on, women helped make American movies as filmmakers, performers and consumers. Yet by the time movies were called talkies, women had been largely pushed out of directing. Hollywood kept churning out fantasies, but the spirited women of early cinema who had been the heroines of their stories were largely replaced by more familiar domesticated female types, For much of the classical era, films pushed romance as the female aspiration, with stories sealed by a happily ever after kiss.

There are many flavors of cinematic kisses: seductive, chaste, dramatic, playful, erotic, parental. Some are mutual, others less so. The forced kiss of “The Quiet Man” was long popular in Hollywood movies until fairly recently. Most forced kisses convey an erotically charged, borderline or bluntly violent relationship between a woman and a man, who circle each other playfully or guardedly or sneeringly before moving into an intimate clinch. And while sometimes a kiss is just a rough kiss and an overture to a fade to black, at other times it also evokes the sexualized violence and rape that Production Code censors policed from the 1930s through the 1960s.

The forced kiss can be nuanced; much depends on the movie and your point of view, what turns you on cinematically and in other ways. In Samuel Fuller’s “Pickup on South Street,” Richard Widmark’s pickpocket hits Jean Peters’s intruder so hard in the face he knocks her out. He doesn’t know whom he’s hit because the lights are off. Soon enough, the two are steaming up the joint in a fog of erotic violence. In “Blade Runner,” Harrison Ford’s cop chases Sean Young’s replicant, slams shut a door she opens, grabs her and shoves her against a window. He orders her to kiss him, and she obeys. In “Baby Boom,” Diane Keaton’s 1980s former big-city career woman tells off Sam Shepard’s local veterinarian who kisses her as he then pushes her against her car.

The forced kiss suggests a worldview that is no longer fully or at least thoughtlessly permissible, given contemporary consent laws and initiatives like “yes means yes.” And it’s only because of #MeToo that I’ve faced how common forced kisses have been and how often I didn’t give most of them a second thought. Now they jump out at me, reminding me that sexualized violence and its threat has been beautifully directed, and is a way that movies signify relations between men and women. This hasn’t led me to reject certain films and filmmakers. Policing desire isn’t of interest to me, understanding film is.

Lesson 2: Women Need a Spanking

In movies, male domination sometimes includes punishment that’s framed as playful. In “The Thin Man Goes Home,” Nick Charles spanks his wife, Nora, with a newspaper and she jokes about wife beating. John Wayne spanks Elizabeth Allen in “Donovan’s Reef” and Maureen O’Hara in “McLintock!” One of his screenwriters once said: “All you gotta have in a John Wayne picture is a hoity-toity dame with big tits that Duke can throw over his knee and spank.” In “Blue Hawaii,” Elvis saves a would-be suicidal woman, whom he then vigorously spanks. Afterward, they eat happily together with her seated on pillows, presumably because her rear is now sore.

In the “Fifty Shades of Grey” movies, sexual domination has been commodified and a designer dungeon is merely part of an aspirational lifestyle for mind-numbingly boring heterosexuals. Kink aside, the movies speak to an ambivalence about power, women and men evident in both female-driven stories and male ones, which also dominate the box office. This might be easier to tolerate if fewer movies stuck women in the same gender box, like the waiting woman who, similarly to Odysseus’ wife, Penelope, stays home while he goes off on his adventure. In “In the Heart of the Sea,” Charlotte Riley asks Chris Hemsworth to promise that he will “come back” to her. He does, alas.

Lesson 3: Women Live to Support Men

In “In the Heart of the Sea,” Riley plays both the waiting wife and another thankless stereotype: the cheerleader wife who signals the hero’s heterosexual bona fides and delivers support. “If you don’t speak for them, who will?” the wife played by Gugu Mbatha-Raw asks her pathologist husband (Will Smith) in “Concussion.” He’s on the verge of greatness, and she exists to help him achieve it. Other filmmakers try to expand the wifely role, as Damien Chazelle does in the Neil Armstrong biopic “First Man.” But while he gives Claire Foy’s wife, Janet, screen time, it’s her husband (Ryan Gosling) who rockets to the moon, and Chazelle never manages to make these two realities equal.

He tries, mainly through a child’s death that humanizes Neil. But by lingering on grief’s impact on Neil, Chazelle sidelines Janet and her role in her husband’s emotional and psychological life. In the end, the child’s death blurs with all the other deaths in Neil’s life — all his losses — which shifts “First Man” into familiar terrain as it becomes another story of male sacrifice, triumph and redemption. Like many filmmakers, Chazelle flounders in the domestic realm here. He fails to show what the heroic journey means for the men who leave and for the women and children who stay behind, a divide that James Gray radically explores in “The Lost City of Z.”

Lesson 4: Women Can Transcend Stereotypes

Of course, if movies were all bad, we wouldn’t love them; I couldn’t love them. One of their miracles is that despite everything, they bring us sublime female characters who surmount often degrading stereotypes and lavish, punishing abuse. This ambivalence fuels the 1937 weepie “Stella Dallas,” in which Barbara Stanwyck’s good-time gal suffers for being her. But Stella is indomitable, like many memorable female characters, and her strength of will connects her to later heroines like Ripley in the “Alien” franchise. Stanwyck’s performance along with her radiant charisma and her humanity convey a fullness of female life that many movies have tried — and still try — to deny.

A few years ago, I reread Molly Haskell’s 1974 book, “From Reverence to Rape,” which remains relevant as a guide for how women can love the movies without surrendering their politics or self-respect. Haskell observed that although the male-dominated industry did its part to keep women in their place, female writers and editors continued to shape cinema, as did female stars. These “love goddesses, mothers, martyrs” embodied stereotypes that they also at times transcended. I had already learned this lesson from watching the movies, which I passionately loved, grew to hate and had to learn to love again.

Pretty in Pink (1986)

Molly Ringwald didn’t have to change to become worthy of a boy — he had to change to end up with the hero (her). I did always wonder what she saw in Andrew McCarthy.

HEIDI MUELLER, Chicago

Real Women Have Curves (2002)

I hated my body at 12. Seeing someone onscreen who looked like me, loving herself, being so confident in her own skin was formative.

CAROLINA KAMMEL, Durham, N.C.

Ghostbusters (1984)

Sigourney Weaver’s self-contained cellist taught me there was a future that didn’t necessarily involve marriage and kids

CLEM BASTOW, Melbourne, Australia

Contact (1997)

That I could be an awesome astronomer, and science is also made by/for women.

ROSANA HINOJOSA, via Instagram

The Devil Wears Prada (2006)

Don’t let that career step on your man’s toes. He’ll leave you.

J. BORG, New Jersey

13 Going on 30 (2004)

To be successful in your career you need to be mean. And if you’re mean, then you can’t get the guy.

VAISHNAVI VAIDYA, Philadelphia

Every Romantic Comedy Ever

That your life is over when you get married.

JENNIFER HAMLIN, via Instagram

Every Horror Movie Ever

Women who don’t behave modestly deserve ridicule, disrespect and “whatever they get,” in most cases the first death.

MELANIE ROGERS, Australia

Lesson 5: Women Can Be Heroes

When I was a kid, that love was unconditional. I watched everything, often alone in theaters. (In the 1970s, my pre-helicopter-era parents didn’t monitor my filmgoing.) Then, as now, a lot of what I watched were movies about men. But I always saw the women, the funny and sad ones, the weak and the strong, those who survived to the end and those who didn’t. I adored performers like Cicely Tyson in “Sounder,” a childhood favorite, and Shelley Winters in “The Poseidon Adventure,” radically distinct characters who remained with me because they were strong but also because they were strong in recognizably human ways. They felt real to me, like people, not decoration.

Feminism complicated my movie love and eventually enriched it. First, I had to struggle with theoretical orthodoxies, including those about visual pleasure and women in film existing to be looked at by men. A lifetime of watching movies — and their women — told me otherwise. So did discovering female directors like Claire Denis (“Chocolat”), Julie Dash (“Daughters of the Dust”) and Kathryn Bigelow (“Blue Steel”), who offered up new visions of what a woman could do and be onscreen. One pleasure of their work isn’t that the stories are simply female-driven, but that women can be archetypal heroes, a role today still largely played by male characters.

That was a lesson I learned from other favorites, too, like “Thelma & Louise,” though admittedly things didn’t turn out well for them. I prefer to focus on everything that happens before their big drive into the beyond, on all the running amok and fun. Bette Davis bemoaned the finales of some of her films. “Those in charge of studios changed endings after a film was finished as often as they changed titles — both detrimental to our work in the film.” She was right, but few Hollywood endings can erase the preceding 85 or so transporting, liberating minutes, when stars like Davis and West as well as characters like Thelma and Louise own their films — or share them like Ginger Rogers.

Lesson 6: Women Can Be Dangerous

This will surprise no one who knows (or reads) me, but I have a thing for difficult women, who I am drawn to in life and onscreen. I have a particular weakness for the kinds of dangerous, sometimes unhinged femmes fatales in film noirs like “Gun Crazy” and “Out of the Past.” Invariably, women like these are put in their place (and a box in the ground). Yet in many movies, they present a vision of female power, however sexualized and pathological. The story is saying one thing, though sometimes just winking. The magnetic performers and characters convey the overriding fear of women (desire, too), but with visions of female unruliness and a life force that no censor could expunge.

This brings me back to “The Quiet Man.” Perhaps it seems absurd, but I deeply love it despite its sexism and everything that I later learned about John Ford’s abusive behavior toward Maureen O’Hara. As her director, Ford roughs up O’Hara, but she conveys a self-determination that far exceeds the film’s concept of female sovereignty. As an actress, she can’t fix everything, including the suggestion that sexual relations are a struggle for power. But O’Hara’s sympathetic portrait of resolve — her palpable will — is a vision of attenuated female liberation, which is what gives the film its truest glint of realism.

Lesson 7: Women Can Be Complicit

Women in movies are often greater and more complicated than their stories. In “Gone With the Wind,” a film with several forced kisses, Vivien Leigh’s Scarlett suffers, but her pain is meant to seem more profound — and help obscure — the agonies endured by the enslaved characters, including Hattie McDaniel’s Mammy. In many ways, Scarlett is the antithesis of the kind of suffering woman that movies still adore, but her triumph is possible only because of racism, which has long been the offscreen story of white women in Hollywood. Another painful lesson the movies have taught me is that just because a woman is a victim doesn’t mean she isn’t culpable.

Lesson 8: Women Can Speak Out

It took me years to understand how I could do more than try to ignore, laugh off or simply rail about onscreen sexism and racism and all of the innumerable outrages that were — are — always there. I learned to find pleasure despite these paradoxes and sometimes in them, to see beyond the goddess-whore dualities, to sometimes love both the simpering patsies and the shrewish man-eaters. I could ignore the ugliness of the movies, wish away the bad parts or watch selectively. Instead, I accept that movies are one way that people make messy meaning of life, and the greatest thing I could learn from them is to refuse to let them or my equally messy pleasures off the hook.

Here is what else movies have taught me: They rarely get women right. Forced kisses and (most) spankings are no longer freely, carelessly, dispensed, but the power dynamic they represent remains. Instead of lonely male heroes, we sometimes get cartoons of female empowerment, with aspirational princesses and one-dimensional warriors brandishing the same old guns and poses. Sometimes these women have adventures; at other times, they resemble the classic movie wife, mainly there to support the man, except now wearing spandex instead of an apron. Their second-rate status speaks to much that is wrong with movies, yes, but the fault is scarcely the movies’ alone.

Manohla Dargis is the co-chief film critic of The New York Times.

Produced by Alicia DeSantis, Gabriel Gianordoli and Josephine Sedgwick.

Photo Credits (What Readers Learned): MGM, Paramount Pictures, Lucasfilm/20th Century Fox, Silver Screen Collection/Getty Images, 20th Century Fox, Disney, Miramax, Paramount Pictures, HBO Films, Columbia Pictures, Warner Bros., Columbia Pictures, Barry Wetcher/20th Century Fox, Warner Bros. Video Credits (Forced Kisses): Warner Bros., Paramount Pictures, MGM, Warner Bros. Video Credits (Spanking): Warner Bros., Paramount Pictures, Paramount Pictures, Paramount Pictures, Criterion Collection, Universal Pictures. Video Credits (Dangerous Women): Paramount Pictures, United Artists, Warner Bros., MGM.

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Gender stereotypes in hollywood movies and their evolution over time: insights from network analysis.

1. Introduction

2. materials and methods, 2.1. description of data source, 2.2. network construction, 2.2.1. common themes for male and female characters in movies, 2.2.2. the lives of male and female characters in movies, 3.1. common themes for male and female characters in hollywood movies, 3.2. gender-specific story tropes and their evolution, 3.3. roles, actions, and descriptions of male and female characters, 3.3.1. nouns: what roles do male and female characters play, 3.3.2. verbs: what do male and female characters do, 3.3.3. adjectives: how are male and female characters described, 4. discussion, 4.1. community analysis, 4.2. trope analysis, 4.3. edge-weight analysis, 4.4. summary of results, 4.5. real-life implications, 5. limitations, 6. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Kumar, A.M.; Goh, J.Y.Q.; Tan, T.H.H.; Siew, C.S.Q. Gender Stereotypes in Hollywood Movies and Their Evolution over Time: Insights from Network Analysis. Big Data Cogn. Comput. 2022 , 6 , 50. https://doi.org/10.3390/bdcc6020050

Kumar AM, Goh JYQ, Tan THH, Siew CSQ. Gender Stereotypes in Hollywood Movies and Their Evolution over Time: Insights from Network Analysis. Big Data and Cognitive Computing . 2022; 6(2):50. https://doi.org/10.3390/bdcc6020050

Kumar, Arjun M., Jasmine Y. Q. Goh, Tiffany H. H. Tan, and Cynthia S. Q. Siew. 2022. "Gender Stereotypes in Hollywood Movies and Their Evolution over Time: Insights from Network Analysis" Big Data and Cognitive Computing 6, no. 2: 50. https://doi.org/10.3390/bdcc6020050

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• DOI: 10.3390/bdcc6020050
• Corpus ID: 248608696

Gender Stereotypes in Hollywood Movies and Their Evolution over Time: Insights from Network Analysis

• Arjun M. Kumar , Jasmine Y. Q. Goh , +1 author Cynthia S. Q. Siew
• Published in Big Data and Cognitive… 6 May 2022

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Patterns of implicit and explicit stereotypes iii: long-term change in gender stereotypes, the times they are a-changing … or are they not a comparison of gender stereotypes, 1983–2014, disney’s female gender roles: the change of modern culture, transformation of gender roles in hollywood movies: 1946–1990, gender stereotypes., gender stereotypes in natural language: word embeddings show robust consistency across child and adult language corpora of more than 65 million words, gender stereotypes have changed: a cross-temporal meta-analysis of u.s. public opinion polls from 1946 to 2018., video game characters and the socialization of gender roles: young people’s perceptions mirror sexist media depictions, related papers.

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Race and Gender in “Hidden Figures” (2016) Essay (Movie Review)

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Summary of Hidden Figures

Analysis: intersectionality of race and gender, evaluative conclusion: the moral behind hidden figures, works cited.

Events depicted in the movie Hidden Figures (2016, directed by Theodore Melfi) are set in the time when the United States competed with Russia to put a man in space. When working on this task, NASA unexpectedly found talented scientists among the group of African-American women mathematicians who helped the entire organization succeed in reaching its goals. The movie follows the real-life stories of three brilliant and talented women, Katherine Goble, Mary Jackson, and Dorothy Vaughan.

Because of her skills in analytic geometry, Katherine was assigned to assist the Space Task Group under the supervision of Al Harrison; the woman immediately felt the pressure of her predominantly male white colleagues to perform her tasks quickly and efficiently without attracting too much attention to her persona.

Katherine was the first black woman that worked on the team of male engineers in the environment that was quite dismissive of African-American women. Meanwhile, Dorothy was informed that she was not going to get a raise due to her being a representative of the colored group. Mary was able to brilliantly identify a problem in an experimental capsule’s heat shields.

During their work, women had to deal with numerous instances of unfair treatment towards female employees of color, which can be considered the key theme of the movie. Despite this, Katherine managed to get along with her colleagues, who ultimately recognized and praised her for the contributed. Mary convinced the court to allow her to pursue a degree in engineering while Dorothy became a supervisor of the Programming Department.

The movie’s epilogue revealed that Katherine Goble was the one who calculated Apollo 11 and Space Shuttle trajectories and later awarded the Presidential Medal of Freedom in 2015, the same year when NASA called the Computational Building at the Langley Research Center in honor of Katherine Johnson.

The literary element of Hidden Figures’ analysis will be focused on the specific theme: the intersectionality of race and gender. The three women depicted in the movie broke racial and gender barriers to meet their professional and personal goals (“Hidden Figures – At the Intersection of Race, Gender and Technology”). In the movie, racial barriers were more evident, like, for example, in the scene with segregated bathrooms. When assigned to help with calculations on the launch of Space Task Group, Katherine had to work on the east campus (Silman).

There was a scene when she asked her only female (and white) colleague where the restroom was. The woman replied, “I have no idea where your bathroom is” (qtd in Silman). Because of this, Katherine had to experience the humiliation of running half a mile in heels across the campus to visit a “colored bathroom.” It is noteworthy that the director managed to bring the injustices that women of color experienced down to the personal level, which was reflected in the most basic and routine activities such as going to the restroom.

Discussing the restroom scene within the context of the main theme of race and gender in Hidden Figures is important because it showed the tension between the urgent scientific work and the lack of logic associated with the discrimination that limited Katherine as a woman and a mathematician. In this case, segregation is not only an injustice towards a woman of color but also a barrier that prevented one of the brightest American minds from achieving success.

Scenes such as when other engineers put out a “colored” coffee pot for Katherine made modern viewers angry at the injustice and the lack of sensitivity the white men in the department had (Silman). Dorothy also experienced difficulties in being a Black woman in the male-dominated workplace. Throughout the movie, Dorothy’s supervisor Vivian consistently disrespected her and refused to give her the promotion she truly deserved.

However, as the movie progressed, viewers saw the barriers of discrimination against African-American women being destroyed. There was a groundbreaking scene in which Al Harrison (Katherine’s supervisor) broke down the sign that said: “colored bathroom” (Heathman). The scene was symbolic since it represented the desire of NASA as an organization to smash the barriers their Black employees had to face.

The three women’s stories may remind viewers of how some people fought for equality in marches of protest while others fought a different battle in office buildings by trying to prove their skills and value to those people who were not better than them in any way (“Hidden Figures – At the Intersection of Race, Gender and Technology”).

If to apply Daniel Bonevac’s “Making Moral Arguments” to the analysis and evaluation of Hidden Figures , it is important to differentiate between factual and moral premises that will lead to a conclusion and forming of an argument. In the case of Hidden Figures , the factual premise used for the formation of the government was that African-American women were discriminated against in the workplace, even when working on projects of governmental significance.

The moral premise is that mistreating individuals based on their skin color or gender is wrong because these characteristics do not affect their value as productive workers. Therefore, women of color should not experience discrimination, which was the principal argument of Hidden Figures overall. Evaluating the significance of the movie is impossible without stating that discussions about race and gender are still relevant to this day. While African-American women can hold any position in society and achieve success in life, it is important to remember that five decades ago they did not have this kind of freedom.

The understanding of Hidden Figures in the light of watching changed dramatically compared with the first impression because the movie did not resort to over-exaggeration and did not make a mistake of suggesting that racism completely disappeared when the “colored bathroom” sign was removed. It was unexpected since too many movies present a stereotypical scenario of a happy ending without acknowledging the historical facts (Cruz).

It is crucial to mention that after Katherine’s, Mary’s, and Dorothy’s success, women of color were still oppressed and perceived as inferior. Even today women of color working in STEM fields are more likely to be forced to prove themselves to their colleagues (Gupta).

To conclude, Hidden Figures is a remarkable story of the victory of intelligence over bias and prejudice. The depiction of the mundane events that occurred in the workplace showed that even the brightest minds were once put in a box and forced to follow the illogical rules that made no sense. It is recommended to watch the movie to enrich one’s knowledge of African-American experiences at the times of segregation of exclusion.

Cruz, Lenika. “What Sets the Smart Heroines of Hidden Figures Apart.” The Atlantic . 2017. Web.

Gupta, Shalene. “Study: 100% of Women of Color in STEM Experience Bias.” Fortune . 2015. Web.

Heathman, Amelia. “Hidden Figures: The True Story Behind the Women who Changed NASA’s Place in the Space Race.” Wired . 2017. Web.

“Hidden Figures – At the Intersection of Race, Gender, and Technology.” IBM . 2017. Web.

Silman, Anna. “Hidden Figures Shows How a Bathroom Break Can Change History.” The Cut . 2017. Web.

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Annual Review of Developmental Psychology

Volume 2, 2020, review article, media and the development of gender role stereotypes.

• L. Monique Ward 1 , and Petal Grower 1
• View Affiliations Hide Affiliations Affiliations: Department of Psychology, University of Michigan, Ann Arbor, Michigan 48109, USA; email: [email protected]
• Vol. 2:177-199 (Volume publication date December 2020) https://doi.org/10.1146/annurev-devpsych-051120-010630
• First published as a Review in Advance on September 15, 2020
• Copyright © 2020 by Annual Reviews. All rights reserved

This review summarizes recent findings (2000–2020) concerning media's contributions to the development of gender stereotypes in children and adolescents. Content analyses document that there continues to be an underrepresentation of women and a misrepresentation of femininity and masculinity in mainstream media, although some positive changes are noted. Concerning the strength of media's impact, findings from three meta-analyses indicate a small but consistent association between frequent television viewing and expressing more stereotypic beliefs about gender. Concerning the nature of these effects, analyses indicate significant connections between young people's screen media use and their general gender role attitudes; their beliefs about the importance of appearance for girls and women; their stereotyping of toys, activities, and occupations; and their support for traditional sexual roles. We offer several approaches for moving this field forward, including incorporating additional theories (e.g., stereotype threat), focusing more on boys and ethnic minority youth, and centering developmental milestones.

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• Article Type: Review Article

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How Accurate are Gender Stereotypes?Evidence from Multiple Criteria and Multiple Methods

25 Pages Posted: 23 Aug 2024

Alice Eagly

Northwestern University

Judith A. Hall

Northeastern University - Department of Psychology

Social perception accuracy includes stereotype accuracy, defined as holding correct beliefs about social groups. The present review examines this type of accuracy in relation to gender stereotypes, defined by perceived differences between women and men. After locating all studies reporting comparisons between judges’ stereotypes and relevant criterion data, we summarized their results and conducted original analyses of the raw data reported in studies. Comparing these estimates to the criteria, we found high accuracy about the female versus male direction of differences, with 84% of 653 estimations of gender differences aligning with criteria. Consensual sensitivity correlations that assessed judges’ collective awareness of the relative size and direction of the criterion differences also favored accuracy with a mean correlation of .76. Analysis of bias in these beliefs revealed both under- and overestimation of the differences, depending on the type of criterion. This review’s finding of good evidence for stereotype accuracy is consistent with the extensive exposure men and women have to other men and women in daily life.

Keywords: gender, stereotype accuracy, gender stereotypes, gender differences, sex differences

Suggested Citation: Suggested Citation

Alice Eagly (Contact Author)

Northwestern university ( email ).

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Northeastern University - Department of Psychology ( email )

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