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• Published: 13 May 2020

Using data science to understand the film industry’s gender gap

• Dima Kagan   ORCID: orcid.org/0000-0002-8216-8776 1 ,
• Thomas Chesney 2 &
• Michael Fire 1  

Palgrave Communications volume  6 , Article number:  92 ( 2020 ) Cite this article

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• Complex networks
• Cultural and media studies

Data science can offer answers to a wide range of social science questions. 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. 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). Analyzing this data, we investigated gender bias in on-screen female characters over the past century. 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. 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. Here we propose a new and better alternative to this test for evaluating female roles in movies. Our study introduces fresh data, an open-code framework, and novel techniques that present new opportunities in the research and analysis of movies.

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Introduction.

The film industry is one of the strongest branches of the media, reaching billions of viewers worldwide (MPAA, 2018 ; UNIC, 2017 ). 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 ). In particular, the representation of women in media has an enormous influence on society. 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 ).

Movies are the fulfillment of the vision of the movie director, who controls all aspects of the filming. It is well known that movie directors are primarily white and male (Smith et al., 2017 ). 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 ). 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 ). 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 ).

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. 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 ).

In this study, we present Subs2Network , a novel algorithm to construct a movie character’s social network. We demonstrate possible utilizations of Subs2Network by employing the latest data science tools to comprehensively analyze gender in movies (see Fig. 1 Footnote 1 ). 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.

The evolution of female representation in the Star Wars movies series.

The study’s primary goals are to answer the following four questions:

Question 1: Are there movie genres that do not exhibit a gender gap?

Question 2: What do characters’ relationships reveal about gender, and how has this changed over time?

Question 3: Are women receiving more central movie roles today than in the past?

Question 4: How has the fairness of female representation in movies changed over the years?

To answer these questions, we first analyzed movie subtitles using text-processing algorithms and a list of movie characters’ names (see Fig. 2 ). We then developed Subs2Network to construct a movie character’s social network. We created an open-source code framework to collect and analyze movie data, and we used this framework to construct the largest open movie social network dataset that exists today.

Turning subtitles into a network, step by step: a perform named entity recognition on the subtitles; b match the entities to the movie characters; and c link the characters and increase the edge weight by one.

Using the constructed movie social networks, we extracted dozens of topological features that characterized each movie. By analyzing these features, we could observe the gender gap across movie genres and over the last 99 years. 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 )).

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 . These differences indicate that men are getting more central roles in movies than women (see Fig. 2a, b , and section “Results”). 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 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. Our results highlight how and where gender bias manifests in the film industry and provides an automatic way to evaluate it over time.

The key contributions presented in this paper are fivefold:

A novel algorithm (see section “Methods and experiments”) which utilizes movie subtitles and character lists to automatically construct a movie’s social network (see section “Constructing movie social networks” and Fig. 2 ).

The largest open movie social network dataset, 21 times larger than the previous dataset (Kaminski et al., 2018 ) (see section “Datasets”). Our dataset contains 15,540 dynamic networks of movies (937 of these networks are networks of biographic movies, which have information about real-world events).

An open-source framework for movie analysis. The code contains a framework to generate additional social networks of movies, facilitating research by creating and analyzing larger amounts of data than ever before.

A machine-learning classifier that can predict if a movie passes the Bechdel test (see section “Constructing the Bechdel test classifier”) and can evaluate the change in gender bias in thousands of movies over several decades (see section “Results”).

Our new and alternative automated Bechdel test to measure female representation in movies. This new test overcomes the weaknesses of the original Bechdel test.

Our study demonstrates that inequality is still widespread in the film industry. In movies of 2018, a median of 30% women and a mean of 33% were found in each movie’s top-10 most central roles. That being said, there is evidence that the gender gap is improving (see Fig. 3 ).

The change in the percentage of women in top 1, 3, and 10 most central roles over time.

The remainder of this paper is organized as follows: In section “Related work”, we present an overview of relevant studies. In section “Methods and experiments”, we describe the datasets, methods, algorithms, and experiments used throughout this study. In section “Results”, we present our results. Then, in section “Discussion”, we discuss the obtained results. Lastly, in section “Conclusions”, we present our conclusions from this study and offer future research directions.

Related work

Movie social networks.

In the past decade, the study of social networks has gained massive popularity. Researchers have discovered that SNA techniques can be used in many domains that do not have explicit data with a network structure. One such domain is the film industry. Researchers have applied SNA to analyze movies, gaining not only new insights about specific movies but also about the film industry in general. For example, using social networks makes it possible to empirically analyze social ties between movie characters.

In 2009, Weng et al. ( 2009 ) presented RoleNet, a method to convert a movie into a social network. The RoleNet algorithm builds a network by connecting links between characters that appear in the same scene. RoleNet is based on using image processing for scene detection and face recognition to find character appearances. Weng et al. evaluated their method on 10 movies and three TV shows. The method was used to perform semantic analysis of movies, find communities, detect leading roles, and determinine story segmentation.

In 2012, Park et al. ( 2012 ) developed Character-net, another method to convert movies to networks. Character-net builds the social network based on dialog between characters, using script–subtitle alignment to extract who speaks to whom in the scene. Park et al. ( 2012 ) evaluated their method on 13 movies. Similar to RoleNet, Character-net was used to detect leading roles and to cluster communities.

In 2014, Agarwal et al. ( 2014 ) presented a method for parsing screenplays by utilizing machine-learning algorithms instead of using regular expressions. Their study showed that the parsed screenplay can be used to create a social network of character interactions. In 2015, Tran and Jung ( 2015 ) developed the CoCharNet, a method which adds weight to a link in the interaction network, where the weight is a function of the number of times two characters appear together. Tran and Jung used CoCharNet to evaluate the importance of characters in movies. They demonstrated that network centrality features such as closeness centrality, betweenness centrality, and weighted degree can be used to classify minor and main characters in a movie. For instance, they detected the main characters using closeness centrality with a precision of 74.16%.

In 2018, Lv et al. ( 2018 ) developed an algorithm to improve the accuracy of creating social networks of movies. They presented StoryRoleNet, which combines video and subtitle analysis to build a more accurate movie social network. The subtitles were used to add additional links that the video analysis might miss. Similar to RoleNet and Character-net, Lv et al. ( 2018 ) used the movie social networks to cluster communities and to detect important roles. They evaluated the StoryRoleNet method on three movies and one TV series, for which they manually created baseline networks (Lv et al., 2018 ).

Also in 2018, a dataset from Moviegalaxies (Kaminski et al., 2018 ) Footnote 2 was released. Moviegalaxies is a website that displays social networks of movie characters. The dataset contains 773 movie social networks that were constructed based on movie scripts. However, Moviegalaxies did not disclose the exact methods which were used for the construction of the networks.

Evaluating the gender gap

In recent years, there have been many studies that attempt to evaluate the gender gap between males and females across various domains (Jia et al., 2016 ; Larivière et al., 2013 ; Lauzen, 2018b ; Wagner et al., 2015 ). For example, in 2018 the World Bank evaluated that the costs of gender bias are vast; gender inequality results in an estimated $160.2 trillion loss in human capital wealth (Worldbank, 2018 ).

Over the years, researchers have discovered many manifestations of the gender gap in our society. Larivière et al. ( 2013 ) discovered that scientific articles with women in dominant author positions receive fewer citations. Wagner et al. ( 2015 ) observed that men and women are covered equally on Wikipedia, but they also discovered that women on Wikipedia are portrayed differently from men. Jia et al. ( 2016 ) found that in online newspapers, women are underrepresented both in text and images.

The state of women in the film industry is similar to other domains: women are underrepresented and badly portrayed (Lauzen, 2018b ; Wood, 1994 ). The Boxed In 2017–18 report (Lauzen, 2018b ) observed a 2% decline in female major characters across all platforms, compared to the previous year.

To tackle the underrepresentation of women in movies in 1985, the cartoonist Alison Bechdel published a test in her comic strip Dykes to Watch Out For to assess how fairly women are presented in filmed media. The Bechdel–Wallace test (Bechdel, 1985 ) (denoted as the Bechdel test ) has three rules that a movie has to pass to be considered “women friendly”:

It has to have at least two women in it.

The women have to talk to each other.

The women must talk about something besides a man.

To Bechdel’s surprise, the media adopted her joke, and today it is a standard for female representation in movies (Douglas, 2017 ; Morlan, 2014 ; Hickey, 2014 ; Shift7, 2018 ; O’Hare, 2017 ). Today the Bechdel test is considered to be the mainstream benchmark for assessing the fairness of female representation in movies and today only 57% of current movies pass this test. Additionally, it is currently the only test that has available labeled data for about 8000 (Fest, 2019 ) out of 516,726 movies available on IMDb (IMDb, 2019b ).

The Bechdel test is also used by researchers. In recent years, studies have utilized the test to evaluate gender bias in movies. In 2014, Garcia et al. ( 2014 ) quantified the Bechdel test and also applied it to social media. They joined YouTube trailers, movie scripts, and Twitter data, which resulted in 704 trailers for 493 movies and 2970 Twitter shares. Garcia et al. created a social network of dialogues for these movies. Additionally, they constructed a network of dialogues between Twitter users who discussed the trailers. They mapped dialogues between men who were referring to women and between women who were referring to men. This mapping was used to calculate the Bechdel score. They found that trailers of movies which are male biased are more popular. Also, they discovered that Twitter dialogues have a similar bias to movie dialogues (Garcia et al., 2014 ).

In 2015, Agarwal et al. ( 2015 ) studied the differences between movies that pass and fail the Bechdel test. Similar to Garcia et al., Agarwal et al. also constructed social networks using screenplays. They created a classifier to automate the Bechdel test, which was trained on 367 movies and evaluated on 90. In the evaluation, they discovered that network-based features perform better than linguistic features. Additionally, they discovered that movies that fail the Bechdel test tend to have women in less central roles (Agarwal et al., 2015 ). With this being said, the Bechdel test has several major flaws. The test does not take into account if women are represented stereotypically (Waletzko, 2017 ). Additionally, there are movies that are considered feminist but do not pass the test (Florio, 2019 ). Moreover, the test is considered to be a low threshold since a film can pass the test with a single line of dialogue between two women (Shift7, 2018 ).

In 2017, Ramakrishna et al. ( 2017 ) utilized screenplays to study the differences in the portrayal of characters in movies. For the analysis, they used 945 screenplays. Mainly they performed linguistical analysis to capture gender stereotypes. They discovered that movies with female directors have less gender-biased casts. Also, they found that female characters use more positive language than males. Additionally, they constructed social networks from the screenplays and performed centrality analysis. The networks in the study were constructed. For the construction of the networks they used a method that was originally developed for converting books into social networks. In the same year, Sap et al. ( 2017 ) used connotation frames to study gender bias in films. They performed their analysis on 7772 movie screenplays, discovering that men were portrayed to have more authority than women. Additionally, they studied the relationship between connotation frames and the Bechdel test. Surprisingly, they found that movies where female characters speak with high agency are less likely to pass the Bechdel test.

Graph features and named entity recognition

Data science tools and techniques have evolved rapidly in the past couple of years (Donoho, 2015 ). In this study, we primarily utilized data science algorithms from the domains of natural language processing (NLP) and SNA to computationally analyze movie content, movie social network structure, and how movie features change over time.

Namely, we used NLP to extract character names from the movie subtitles by utilizing named entity extraction (NER) algorithms (Nadeau and Sekine, 2007 ). We used both Stanford Named Entity Recognizer (Finkel et al., 2005 ) and spaCy Python Package (Honnibal and Montani, 2017 ) to find where characters appear in the text.

To match characters’ names in the subtitles with characters’ full names, we utilized FuzzyWuzzy (Fuzzywuzzy, 2019 ), a Python package for fuzzy string matching. Specifically, we used FuzzyWuzzy’s WRatio (Fuzzywuzzy, 2018 ), a method for measuring the similarity between strings. WRatio uses several different preprocessing methods that rebuild the strings and compare them using Levenshtein distance (Levenshtein, 1966 ). Also, WRatio takes into account the ratio between the string lengths.

After extracting the movie characters, we constructed the movie social networks and used various graph centrality algorithms, such as closeness, betweenness, degree centrality, and PageRank (Brandes and Erlebach, 2005 ) to identify the most central characters in each constructed movie network.

Methods and experiments

Constructing movie social networks.

One of this study’s primary goals was to develop a straightforward algorithm that would construct the social network of character interaction within a given movie. We achieved this goal by utilizing movie subtitles Footnote 3 and a list of movie character names. Namely, given a movie, we constructed the movie social network G  := 〈 V , E 〉, where V is the network’s vertices set, and E is the set of links among the network’s vertices. Each vertex v   ∈   V is defined to be a character in the movie. Each link e  := ( u , v , w )  ∈   E is defined as the interaction between two movie characters u and v , w times. For a movie with a given subtitle text and a given character list, we constructed the movie’s social network using the following steps (see Fig. 2 ):

First, we detected when each character appeared in the subtitles. To extract the characters from the subtitles we used NER, extracting all the entities which were labeled as a person or an organization. Additionally, for each entity, we stored the time the entity appeared in the movie.

Next, we matched the entities found in the subtitles with the character list. It worth mentioning that it is not possible to map one-to-one between the characters in the character list and the characters extracted from the subtitle. For example, in the movie The Dark Knight , Bruce Wayne was referred to as “Bruce Wayne” 3 times, as “Bruce” 16 times, and as “Wayne” 20 times.

To address the matching problem, we proposed the following mapping heuristic (see Algorithm 1). First, we split all the roles into first and last names and linked them to the actor and the character’s full name (line 2). Then, if there was only one character with a certain first or last name (one-to-one match), we linked to the character all its occurrences in the subtitles (lines 3–5). However, if we had several characters with the same first or last name, we did not always know who was referred to in the text. For example, in the movie Back to The Future there are three characters with the last name McFly; where only “McFly” was mentioned in the text, we could not determine which character was referenced. Another challenge we encountered was when only part of the character’s name was used. For instance, in the movie The Godfather , the main character is Don Vito Corleone, but he was never mentioned once by his full name because he usually was referred to as “Don Corleone.” Moreover, there are other Corleone family members in the movie. To overcome this challenge, we used WRatio to compare strings and match parts of a name to the full name. Using WRatio , we chose the highest matching character that received a score higher than Threshold (line 6).

In fact, we were able to overcome many of these problems by using hearing-impaired subtitles. In many hearing-impaired subtitles, the name of the speaking character is part of the text. This property allowed us to avoid most the problems we described earlier and gain additional information. For instance, the movie The Matrix has a scene in which Morpheus calls Neo, and we can know this only because of the tag [PHONE RINGS]. Afterward, there is an annotation “MORPHEUS:” which tells us that Morpheus is the one calling. Without this annotation, we could not know who is on the other end of the line (see Fig. 4 ).

The textual format of subtitles in the SubRip format with additional data for hear-impaired. For example, the speaking charachter name, sounds in a textual fromat, etc.

Using the matched characters, we created a link between characters u and v if they appeared in the movie in a time interval less than threshold t seconds ( t was defined as 60). For each such appearance, we increased the weight w between u and v by one. Since in subtitles we do not have an indication of when each scene begins and ends, we used a heuristic to model the interaction between characters. We assumed that two characters who appear one after another in a short period of time probably relate. For example, in Fig. 2 we have part of the subtitles from the movie The Matrix . Morpheus introduces himself to Neo, and we know that Morpheus and Neo are talking within an interval of 5 s. Since, 5 s was smaller than the threshold, we increased the link weight between Morpheus and Neo by one.

To reduce the number of false positive edges, we filtered all the edges with weight lower than w min ( w min was defined as 3). There were two main reasons for the formation of edges that did not exist in the movie. The first case was when we matched an entity to the wrong character. The second case happened when in the interval of t seconds there was more than one scene. These kinds of false positive links add noise to the graph. Most of these links have a very low weight; hence, filtering edges with weight lower than w min helps remove false positive links.

Evaluations of constructed networks

In addition to constructing movie social networks, we also empirically quantified the quality of these networks. Evaluating movie networks is a challenging task. Creating a perfect ground truth is a manual and unscalable process. It requires spending several hours for each movie to manually create ground truth networks. In previous studies (Weng et al., 2009 ; Park et al., 2012 ; Tran and Jung, 2015 ; Lv et al., 2018 ), manually labeling of movies has been done at a very small scale with only several movies (see section “Related work”). Another option is to use the IMDb or TMDB datasets character lists as a ground truth to evaluate only the network nodes. However, these lists contain mostly unnamed characters that are impossible to detect, for example, Guard #2. To solve this issue we could try using name datasets to filter these lists, but we will lose many characters that have foreign names or characters with unreal names like Batman, Superman, etc. To evaluate the quality of the constructed networks without the presented issues, we compared them to other publicly available movie network datasets. Since it is challenging to manually annotate movies, most of the studies only compared their networks to a handful of manually annotated ground truth networks (see section “Related work”).

In this study, to the best of our knowledge, we performed the first large-scale, fully automatic comparison between movie networks. For the comparison, we used a dataset published in 2018 by Kaminski et al. ( 2018 ) (denoted as ScriptNetwork ); this is the only other publicly available movie social network dataset. The ScriptNetwork dataset is based on screenplays and can be considered as much easier content to parse than subtitles. Screenplays have additional information such as the exact name of the character who speaks in the scene even if this character is unnamed. For example, freckled kid is a character in the X-Men (2000) screenplay; unnamed characters like freckled kid are almost impossible to detect in regular texts like books or subtitles. Screenplays can be considered very close to the ground truth. However, screenplays sometimes have big differences with the final movie. For instance, in many screenplays, there are missing and even additional characters (see section “Discussion”).

To evaluate Subs2Network -constructed networks, we performed two types of evaluations:

Central character analysis : We tested if the most central roles in Subs2Network are actually the most central roles in the movie. As a ground truth, we used the IMDb ranking list similarly to Tran and Jung ( 2015 ). The IMDb characters list is ordered the same way as movie credits, which are ordered alphabetically or by the order of appearance (IMDb, 2019a ). For the evolution, we filtered out all the movies where the credits were in alphabetical order, which was only 1%. The actor rank in the credits is considered to be a direct indication of the actor’s power and prestige (Rossman et al., 2010 ). Furthermore, it is very rare for an actor not in the top-10 credited roles to be nominated for an Academy Award (Rossman et al., 2010 ). In other words this indicates that in most movies the credit order has a significance, and the top-10 movie credits are likely to include most of the central characters.

We tested if the top-5 and top-10 ranked nodes (characters) at Subs2Network are the top-5 and top-10 ranked on IMDb. Additionally, we performed the same test on networks constructed from screenplays (Kaminski et al., 2018 ). Our motivation behind this experiment was to verify that Subs2Network’s networks contain the most significant characters in the movie.

Network coverage : We tested if the edges in Subs2Network are the same edges as in other movie networks. For each movie, we created two sub-graphs containing the characters that exist in both networks. Then we calculated the edge coverage in the created sub-graphs. Given two graphs G and H , we define the edge coverage as \({\mathrm {{Coverage}}}_H(G) = \frac{{|E_G\, \cap \,E_H|}}{{|E_H|}}\) . We calculated Coverage Subs2Network ( ScriptNetwork ) and Coverage ScriptNetwork ( Subs2Network ).

In addition to using the Kaminski et al. ( 2018 ) dataset for the network evaluation, we also constructed a small dataset of 15 character co-appearance networks utilizing Amazon X-Ray (Stiffler and Sampaco, 2018 ). The movies in the dataset were selected randomly from the Amazon Prime TV main page, Footnote 4 which includes the most popular movies in the platform. The dataset was constructed semi-automatically in the following way: given a movie, we define the movie ’ s social network graph G xray  := 〈 V xray , E xray 〉. Similar to Subs2Network , each character in the movie is represented as a vertex v   ∈   V xray . Edges are defined as two characters that appear in the same scene according to Amazon X-Ray data. Namely, the set of movie edges E xray is defined to be \(E_{\mathrm {{xray}}}: = \{ (u,v,w)|u,v \in V_{\mathrm {{xray}}}\}\) , where w is the number of scenes in which u and v appeared in the same scene. Additionally, as with Subs2Network , we filtered all the edges with weights lower than 3. Similarly to our comparison with the Kaminski et al. ( 2018 ) dataset, we also calculated Network Coverage. Additionally, we used the fact that Amazon X-Ray is based on the finished movie, which includes additional data such as the time the character appeared in the movie. By utilizing G xray , we analyzed how well Subs2Network contains characters by their screen time. To this end, we calculated the total screen time (denoted as screen( v )) of each character in the X-Ray dataset and divided the characters into deciles according to their screen time. Lastly, we calculated for each decile, d i , i  = 1..10, the percentage of characters that were detected by the Subs2Network algorithm, out of all the characters that were detected by Amazon X-Ray and had screen time in the d i decile. Namely, for each d i , we calculated \({CharCover}(d_i) = \frac{{|V_{Subs2Network}\, \cap\, \{ v \in V_{\mathrm {{xray}}}| {screen}(v)\, \in \,d_i\} |}}{{\{ v\, \in \,V_{\mathrm {{xray}}}| {screen}(v)\, \in \,d_i\} }}\) .

To evaluate and test our movie social network construction algorithm described above on real-world data, we assembled large-scale datasets of movie subtitles and movie character lists. In addition, we collected movie character lists from the IMDb (Internet Movie Database) website Footnote 5 and movie subtitles from 15,540 movies. Furthermore, we also used data from Bechdel test scores of 4658 movies. In the following subsections, we describe in detail the datasets we used.

IMDb dataset

To collect movie and actor data, we used IMDb, which is an online site that contains information related to movies, TV series, video games, etc. (IMDb, 2019b ). IMDb data is contributed by users worldwide. It contains 5,487,394 titles from which 505,380 are full-length movies (IMDb, n.d. ). In this study, we used the official IMDb dataset. Footnote 6 From the IMDb dataset, which contains only a subset of the IMDb database, we mainly used movies’ titles, crews, and ratings data.

Subtitle dataset

To inspect gender bias in movies, we decided to extract information out of subtitles. Subtitles are freely and widely available online on numerous sites. For instance, OpenSubtitles.org Footnote 7 alone hosts more than 500,000 English subtitles (opensubtitles.org, 2019 ) that were manually created by the community. We collected the subtitles using Subliminal Footnote 8 , a Python library for searching and downloading subtitles. Subliminal downloads subtitles from multiple sources, and using an internal scoring method, it decides which subtitles are the best for a specific movie. Using Subliminal, we downloaded subtitles for 15,540 movies.

Bechdel test dataset

Bechdel test data is available at Bechdel Test Movie List Footnote 9 , which is a community-operated website where people can label movies’ Bechdel scores. Using the Bechdel Test Movie List API, we downloaded a dataset that contains 7871 movies with labeled Bechdel scores, from which only 7322 are full-length movies.

Even for humans, it is a challenging task to determine if a movie actually passes the Bechdel test; Bechdeltest.com has a comments section where users discuss the scores and their disagreements (Agarwal et al., 2015 ). For example, according to Bechdeltest.com, the movie The Dark Knight Rises failed the test. However, by taking a closer look at the community comments, Footnote 10 we noticed users arguing regarding the test results, which are hard to determine.

Dataset preprocessing

The most critical part of building a social network of characters’ interaction is mapping correctly between the characters in subtitles and the characters in the character list. The IMDb character data includes data on even the most minor roles such as a nurse, guard, and thug #1. These nameless minor characters are almost impossible to map correctly to their subtitle appearances. Usually, they just add false positive edges and do not add additional information.

To clean the data from nameless characters, we created a blacklist of minor characters (for a detailed explanation of the blacklist construction process see Section S. 1 ). Additionally, to validate the characters’ names we used TMDb (The Movie Database) Footnote 11 , another community-built movie database. For each character, we matched the IMDb and TMDb data by the actor name. Then, we compared the lengths of the character names and kept the longer one. The usage of the longer names captures more variations of the name and helped us match more occurrences of the character in the subtitles. For example, in the film The Godfather (1972) James Caan portrays Sonny Corleone. Not surprisingly, on IMDb he is called Sonny Corleone, but on TMDB he is named Santino Sonny Corleone. In the film, he is addressed 12 times as Santino. By using the longer name, we can map these instances to the character.

Analyzing movie social networks to identify gender bias

Network features

To study gender bias in movies, we calculated five types of features: vertex features, network features, movie features, gender representation features, and actor features. Through the study, we analyzed how these features change over time. Additionally, we used these features to construct machine-learning classifiers. To create a ground truth for actors’ gender, we had to determine whether each actor was male or female. For most of the characters, we extracted the gender from IMDb similarly to Danescu et al. Danescu-Niculescu-Mizil and Lee ( 2011 ). IMDb has an attribute of “actor” or “actress,” which allowed us to identify gender. As we mentioned earlier, the IMDb dataset is only partial, so to overcome this issue we used a dataset that maps the first name to the gender. Footnote 12 In the rest of this section, we supply the definitions of these features.

Vertex features : For a given v   ∈   V , a neighborhood is defined as a set of v friends, Γ( v ). Following are the formal definitions of the vertex-based features:

Total Weight : The total weight of all the edges, which represents the number of character v appearances in the movie, \({\mathrm {{Total}}}_{\mathrm {w}}(v) = \mathop {\sum}\nolimits_{\{ (v,u,w)|\left( {(v,u,w) \in E} \right.\} } w\) .

Closeness Centrality : The inverse value of the total distance to all the nodes in the graph. It is based on the idea that a node closer to other nodes is more central, \(C_{\mathrm {c}}(v) = \frac{1}{{\mathop {\sum}\nolimits_{v \in V} d (v,u)}}\) Brandes and Erlebach ( 2005 ), where d ( v , u ) is the shortest distance between v and u .

Betweenness Centrality : Represents the number of times that a node is a part of the shortest path between two nodes Brandes and Erlebach ( 2005 ). A junction (node) that is part of more paths is more central, \(C_{\mathrm {b}}(v) = \mathop {\sum}\nolimits_{s,t \in V} {\frac{{\sigma (s,t|v)}}{{\sigma (s,t)}}}\) Brandes and Erlebach ( 2005 ), where v  ≠  s  ≠  t , σ ( s , t ) is the number of those paths passing through some node v .

Degree Centrality: A node that has a higher degree is considered more central, \(C_{\mathrm {d}}(v) = \frac{{|{\mathrm{\Gamma }}(v)|}}{{|V|\, - \,1}}\) Brandes and Erlebach ( 2005 ).

Clustering : Measures link formation between neighboring nodes, \(C(v) = \frac{{2T(v)}}{{|{\mathrm{\Gamma }}(v)|(|{\mathrm{\Gamma }}(v)|\, - \,1)}}\) (Saramäki et al., 2007 ), where T ( v ) is defined as the number of triangles through vertex v where a triangle is a closed triplet (three vertices that each connect to the other two).

Pagerank: A node centrality measure that takes into account the number and the centrality of the nodes pointing to the current node Brandes and Erlebach ( 2005 ).

Edge Number —the number of edges in the network | E |.

Vertex Number —the number of vertices in the network | V |.

Number of Cliques —the number of maximal cliques in the network Brandes and Erlebach ( 2005 ).

Statistical Network Features —set of features which are based on the vertex features. From these features, we calculate statistical features for the entire network. We calculate the mean, median, standard deviation, minimum, maximum, first quartile, and third quartile.

Gender representation features

Triangles with N women : The number of triangles that contain N females and 3- N males, where N   ∈  1, 2, 3.

Percent of triangles with N women : The percent of triangles that contain N females and 3- N males, where N   ∈  1, 2, 3.

Females in Top-10 roles : The number of females in top-10 roles ordered by PageRank.

Male count: The number of male actors in the movie.

Female count : The number of female actors in the movie.

Movie features:

Release Year —the year when the movie was first aired.

Movie Rating —the rating the movie has on IMDb.

Runtime —the movie total runtime in minutes.

Genres —the movie genre by IMDb.

Number of Votes —number of votes by which the rating was calculated on IMDb.

Actor features:

Actor Birth Year —the year the actor was born.

Actor Death Year —the year the actor died.

Actor Age Filming —the age of the actor when the movie was released ( \(Release\,Year - Actor\,Birth\,Year\) ).

Network feature analysis

To examine the state of the gender gap, in movies generally and by genre in particular, we analyzed only the most popular movies (movies which had more than n votes on IMDb). We analyzed only the most popular movies since they have better, more correct data, and more importantly, better represent the mainstream media. To decide on n , we observed the distribution of movies by year. We found a right-tailed distribution and decided that n  = 2000 should be a large enough number. To answer our first research question—if there are genres that do not show a gender gap (see section “Introduction”)—we calculated vertex and actor features (see section “Network features”) for all the roles. Next, we split the data by gender and movie genre. Finally, we utilized a Mann–Whitney U (Mann and Whitney, 1947 ) test on these features to check if there are statistical differences between the male and female roles in different genres.

To study relationships in movies, and to answer our second question regarding what relationships reveal about gender, we calculated all the relationship triangles in the network and grouped them by the number of women in each triangle. Afterward, we segmented the triangles by genres and how they changed over time.

To investigate the role of centrality by gender, our third research question regarding the centrality of female roles, we calculated PageRank for the nodes in all our movie networks. We analyzed the number of men and women in the top-10 characters in movies and examined how this number has changed over the years.

Constructing the Bechdel test classifier

As we described in section “Related work”, the Bechdel test is used to assess how fairly women are represented in a movie. The test has three criteria:

Are there at least two named women in the movie?

Do the women talk to each other?

Do the women talk about something other than men?

These criteria are hierarchical; hence, if a movie passes the last test, it has passed all of the tests.

To train the classifier, we extracted all the network, vertex, and gender representation features (see section “Network features”). For testing the trained model, we used the 1000 newest movies in the Bechdel test dataset. Footnote 13 The rest of the movies were used as the training set. As for the classifier, we used Random Forest with max depth 5 to avoid overfitting. For the classifier evaluation, we used AUC. This measure presents how many of the results the classifier is confident it classified correctly. Additionally, we compared our results to the results of Agarwal et al. ( 2015 ).

To answer the fourth research question regarding the fairness of female representation, we analyzed the change in the average probability of a movie passing the Bechdel test over time. Additionally, using the Random Forest feature importance, we inspected which feature was the most important for the Bechdel test classification. Finally, we analyzed the change over time by genre.

Alternative test

The Bechdel test has several major shortcomings; for instance, a movie passes the test if it consists of only one sentence between two women who do not speak about a man. For instance, American Pie 2 , which by no means can be considered to be a movie that fairly presents women, passes the Bechdel test in such a way. To offer solutions to the problems with the Bechdel test (see section “Discussion”), we propose a new gender equality test. We believe that a good test can be created by comparing the number of interactions according to each gender. Hence, we propose an interaction test that compares the total degree of male and female nodes. By utilizing over 15,000 movie social networks in our datasets, we observed that in only 16.7% of movies do female characters have an equal or higher total degree than male characters. Moreover, in 55.8% of analyzed movies, the total degree of male characters is at least twice as high as female characters. We think that a good rule of thumb for a movie should be \(0.8\, < \,\frac{ {{TotalDegree}_{\mathrm {F}}}}{{ {TotalDegree}_{\mathrm {M}}}}\, < \,1.2\) . The Gender Degree Ratio test is neither male nor female-biased; it is a gender equality test.

To evaluate the ability of the proposed test to distinguish between gender-biased and gender-equal movies, first we calculated the Gender Degree Ratio for all the movies in our dataset. Next, we performed significance tests between groups of movies with and without gender bias. Before performing the significance tests, we performed a Shapiro–Wilk test on the Gender Degree Ratio scores of our dataset to test if they distributed normally. To create the gender-biased and gender-equal movie lists, we utilized the three following movie lists:

The 100 best feminist films of all time (Rothkopf, 2018 ): From this list we had 67 movies in our dataset (see Section S. 2 ). We used this list to test if feminist movies get higher Gender Degree Ratio scores than the general population of movies.

100 Must see movies: The Essential Men’s Movie Library (McKay and McKay, 2019 )—from this list we had 79 movies in our dataset (see Section S. 2 ). This goal of using this list was to see if our test would give lower scores to male-centric movies than to the general population.

17 Blockbuster movies that surprisingly pass the Bechdel test (Allen, 2019 )—this list contains movies where women are not presented fairly but still pass the Bechdel test. From this list we had 15 in our dataset (see Section S. 2 ). The goal of testing these movies was to validate that they should fail the proposed test.

For the first two lists, we performed a significance test and compared their scores with the general population of movies. Additionally, the third list was used to test if the Gender Degree Ratio dealt with the shortcomings of the Bechdel test, specifically whether a movie with poor female representation yet passed the Bechdel test would fail our suggested ratio test.

To analyze the gender gap in the film industry, we analyzed subtitles of movies that had at least 1000 votes on IMDb. This resulted in a dataset containing 15,540 movies, which is a dataset 20 times bigger than the largest movie dataset currently available (Kaminski et al., 2018 ).

First, we analyzed the gender gap, in general, and by genres, in particular (see Tables S 1 and S 2 ). We found that the genres with the largest number of features that are distributed similarly between men and women are film-noir, history, horror, music, musical, mystery, and war. In these genres, 9 out of 10 features distribute similarly; only the clustering coefficient distributes differently between men and women. In terms of features, Total Weight and Weighted Betweenness are the features that distribute most similarities between the genders, with 15 out of 21 genres distributing the same. On the other side of the scale, Age Filming is the feature that distributes least similarly, with 0 out of 21 genres distributing similarly.

Second, to examine relationships among characters, we analyzed relationship triangles in the networks. We found that most triangles have three men, and triangles with three women are the least common (see Table 1 ). Out of 21 genres, in 8 genres the most common type of triangle is 3 men (without any women) and in all the others it is 2 men and a woman. According to the results, Romance is the genre with the most interaction among women and War is the genre where women have the least interaction. Inspecting the change in the number of triangles over time (see Fig. 5 ), we can observe that in many genres there is an equalizing improvement over the years, but there are genres like Sport without a big change.

The change in the number of females in relationship triangles for each decaded for different genres.

Third, we analyzed how characters are ranked in terms of centrality (see Table 2 ). We found that among central roles, there are considerably more men than women. For example, men have about twice the roles that ranked in the top-10 most central roles than women. In all top-10 most central roles, the female percentage is the same except for the most central role.

Fourth, we analyzed the gender composition of the top-10 central roles in movies (see Fig. 6 ). We discovered that most of the movies have more men in central roles than women. Moreover, from the data, we can observe that there are almost no movies with no men and 10 women in the top-10 roles. Also, there are a considerable number of movies where the majority of the top-10 most central roles are men.

The distribution of movies by gender of the top-10 most central characters where: a The percentage of movies where out of top-10 role N are of a specific gender. b The number of movies where out of top-10 role N are of a specific gender.

Fifth, we wanted to observe how the percentage of women in top 1, 3 and 10 most central roles has evolved over time. We analyzed the change in this metric over almost from 1965 up to today Footnote 14 (see Fig. 3 ).

It can be seen from the network that there is a constant rise in the number of women in top-10 most central roles.

Sixth, to create an automatic classifier that can assess the fairness of female representation in movies, we created the Bechdel test classifier. Our classifier achieved an AUC of 0.81. We also inspected which feature was more important (see Table 3 ). Seven of 10 features were triangle-based features. Moreover, all the features in the table are a subset of the Gender Representation Features (see section “Network features”).

Next, we trained our automated Bechdel test classifier on all the labeled data and calculated the average probability of the classifier by decade on all the unlabeled data (see Fig. 7 ). We can see that there is a trend of growth. Also, we examined how the probability changed by genres (see Fig. 8 ). Comparing our results to Agarwal et al. ( 2015 ) (see Table 4 ), we found that our classifier performs better than Agarwal’s in terms of F1 score.

Trend line of the average probability of passing the Bechdel test in the past 60 years by decade.

The average probability of a movie passing the Bechdel test by decade and genre.

Afterward, we analyzed the quality of the constructed social networks by comparing Subs2Network with the ScriptNetwork -released networks (Kaminski et al., 2018 ). We observed that the Subs2Network dataset contains 628 out of the 773 networks that appear in the ScriptNetwork dataset. On average, Subs2Network had more central characters than ScriptNetwork from the top-10 most central characters (see Table 5 ); for instance, in the top-10 characters Subs2Network matched 6.06 characters while ScriptNetwork matched 5.35 characters. In terms of edge coverage, we found that Subs2Network covered 65.4% of the edges in ScriptNetwork networks and ScriptNetwork covered 65.1% of the edges in Subs2Network networks. Additionally, we compared Subs2Network with networks we generated based on manually extracted Amazon X-Ray movie data. We observed that Subs2Network matched X-Ray nodes and edges at 79.6% and 54.5%, respectively. Additionally, when analyzing character matching by screen time, we found that we could detect main characters with a high accuracy of up to 96.4% (see Fig. 9 ).

The percent of character that are overlapping between Amazon X-Ray and Subs2Network where the x axis is the screen time of the charcters.

Finally, we analyzed the Gender Degree Ratio test. We found that the average score of all the movies in the dataset was 0.6, meaning there were only 6 female interactions for every 10 male interactions. In fact, we found that today only 12% of all movies pass the gender degree ratio test by having scores between 0.8 and 1.2 (see Fig. 10 ). For instance, Resident Evil: Retribution and The Age of Innocence pass the test with scores of 1.06 and 0.94, respectively. On the other hand, Armageddon and Batman Begins fail the test with scores of 0.2 and 0.24, respectively. To check if the proposed test can distinguish between gender-biased and non-biased movies, we performed significance tests on two groups of movies. First, by performing the Shapiro–Wilk test, we observed that the movie scores were not from a normal distribution. Since the data was not normally distributed, we performed the Mann-Whitney- U test and found that list 1 (feminist movie list) distributed differently from the general population ( μ  = 1.26, p -value = 6.7 × 10 −15 ). Also, we discovered that list 2 (male-biased movie list) scores also distributed differently from the general population ( μ  = 0.34, p- value = 8.5 × 10 −07 ). Regarding the movies that surprisingly passed the Bechdel test, only the movie Grease passed the Gender Degree Ratio test.

The number of movie and the ratio of between female and male characters.

In this study, we present a method that converts movie subtitles into social networks, and we analyze these networks to study gender disparities in the film industry. Using this method, we created the largest available corpus of movie character social networks. The method and the corpus are available for use by other researchers to study additional movies and even TV shows, and it has the potential to revolutionize the study of filmed media.

When looking at relationship triangles, we can see that in 77% of all triangles men are in the majority. In an equal society, we would expect to find that the number of triangles with three men, with three women, and with two men and two women would be the same. However, we discovered that, on average, there are 11.4 times more triangles with three men than with three women, and almost twice as many triangles with two men than two women. At a deeper level of granularity, we can see a difference in the number of triangles between different movie genres. The Romance genre has the highest number of triangles that have two and three women. On the other side of the scale, 90.6% of triangles in the War genre have a majority of men. This result makes sense intuitively. By looking at Fig. S. 1 , we can see that genres with a higher percentage of movies that pass the Bechdel test also have a higher percentage of triangles with a majority of women.

In terms of centrality (see Table 2 ), we can see that men have more central roles than women. We expected to find more females in less central roles, but the percentage of females distributes evenly in the top-10 most central roles. We believe that these results correspond to the total percentage of women in the dataset, which is 32.3% and is very similar to previous studies of Lauzen ( 2018a ) and Sap et al. ( 2017 ). This number is still lower than the total percentage of female roles in IMDb, which is 37.2%.

We also analyzed how many roles in a movie’s top-10 most central roles are those of women. Unsurprisingly, there is a dominance of movies with a majority of men. For instance, all Lord of the Rings movies have 10 men in the top-10 roles. We found only 5 films where all top-10 roles were female, and each of these featured only women (one of these films is called The Women , another movie Caged is about a women’s prison, and the movie The Trouble with Angels is about a girls’ school).

There is also the issue of what is considered fair. Mencarini ( 2014 ) states that fairness in gender context varies between cultures and historical periods. Sometimes women perceive their life as fair from a gender equality perspective while actually it is very low, and sometimes it is exactly the contrary. In a film context, some may argue that it is fair for war movies to have almost no women, while others will argue that it is not fair since women have taken part in all wars. Since fairness is subjective to measure, we used the Bechdel test, which is defined as “the basic measure to see if women are fairly represented in the film” (Fest, 2019 ). Centrality and fairness can sound very similar in the context of films, but they are two different notions. A character can be very central and very stereotypical at the same time. For example, Cinderella is the protagonist (most central character) in her story, but she is cooking and cleaning all day, and her life becomes better only when a rich and handsome prince arrives.

We also presented an automated Bechdel test classifier that can help assess the fairness of how women are presented in movies. We trained our model on data collected from bechdeltest.com, and we have indications that our model is even more accurate than the above presented results. We found that many movies on bechdeltest.com are misclassified. For example, The Young Offenders passes the test on bechdeltest.com (although the site does state this result is ‘dubious’), but our work classifies it as a fail. The reverse is true for the movie Never Let Go . Based on these observations, we believe that our classifier can automatically classify movies with high confidence in the classification. Moreover, while the Bechdel test is certainly a useful and important test, it fails to account for many parameters such as the centrality of the characters, repression, etc. Basically, if there is a movie with only two women who appear in one scene and talk about something other than men for 2 seconds, then the movie will pass the traditional Bechdel test. However, this is the only test that has data that can be used to train a classifier. Our classifier partially tackles this problem since it calculates a score of how strongly the movie passes the test.

To deal with the issues of the Bechdel test, we proposed a new test based on the ratio of the number of female interactions to the number of male interactions in a movie. We found that only 12% of all movies passed our Gender Ratio test (see Fig. 10 ), revealing how dominant gender disparities continue to be in the film industry. As anticipated, we found in our test that feminist movies received higher scores than the average movie. Additionally, we discovered that movies that passed the Bechdel test but did not have good female representation failed the Gender Ratio test, just as we had hoped. These results indicate that our proposed test dealt with some of the major problems of the Bechdel test and has the ability to differentiate between films with good and bad female representation. However, the test is not perfect and does not take into account context. For instance, we can see that Grease passed our test even though women in the film were presented stereotypically.

In future work, we are planning to perform statistical tests to compare the distributions of the degrees of male and female nodes and present a more accurate test. Creating a more accurate assessment of how women are truly represented in films requires manually watching thousands of movies and labeling data, which is impossible with the current research limitations. In the future, we plan to develop a more advanced method based on deep learning to create a better algorithm that will be able to create a much more accurate assessment of movie gender equality, taking into account additional parameters such as the context of the movie.

We also calculated the average probability of passing the Bechdel test for all the movies in our dataset that do not have a Bechdel test score. Afterward, we inspected the change in the average probability of movies passing the test over a long period of time and by different genres. In almost all genres there is a trend of improvement, and there is a correlation between relationship triangles and the Bechdel score. Looking at Fig. 8 , we see that historically war movies have the lowest probability of passing the Bechdel test.

There are many factors that affect our method’s accuracy. The most critical factor is the quality of both the subtitles and the cast information from IMDb. In movies where the name of the character in the subtitles does not correspond to IMDb data, the actor cannot be linked to a character. During our study, we stumbled upon subtitles with spelling mistakes and other inconsistencies. Also, in some movies like superhero movies, we did not know how to link the different identities of a character with names such as “Captain America,” that potentially could be filtered because it looks like a nameless character. In addition, nameless characters like “Street Pedestrian” sometimes eluded our cleaning process. There is a balance between cleaning the IMDb data too much and not enough. We observed that more accurate networks were in movies that had hearing-impaired subtitles since they have additional data and are less affected by the NER accuracy. Some of these limitations will be addressed in future research. Additionally, there are many different improvements that can done to increase the accuracy of the networks; for instance, it is possible to use co-reference resolution, train an NER for subtitles, etc.

One of the biggest challenges of this study was to evaluate the quality of the constructed movie networks. For the evaluation, we compared the networks created by our algorithm with the networks created by screenplay analysis and by Amazon X-Ray. Screenplays have easier content to analyze than subtitles, and they contain plenty of structured information, such as character names, scenes, etc. However, there are also some shortcomings in using screenplays. First, only a small fraction of movies have screenplays available online. Currently, the Internet Movie Script Database (IMSDb) Footnote 15 has only 1198 scripts, while there are hundreds of thousands of movies’ subtitles available online. Moreover, many publicly available screenplays are drafts and have major differences from the actual movies. For instance, the Minority Report Footnote 16 screenplay used by Kaminski et al. is completely different from the movie; almost all the characters’ names are different. Another example can be found in the X-Men (2000) movie where the character Beast appears in the screenplay. However, due to over-budget concerns, Beast was cut from the movie. From inspecting screenplays, we discovered many additional examples of extra, missing, and renamed characters. These problems show that comparing subtitles to screenplays is like comparing apples to oranges. The comparison indicates that there is a similarity between the networks, but it cannot be used as a precise measure of accuracy.

In addition to using screenplays to evaluate the constructed networks, we also used networks that were generated based on Amazon X-Ray. Unlike the screenplays, Amazon X-Ray is based on the finished movie and offers a more accurate representation of the movie’s social network. Using the X-Ray based networks, we found that even though sub2network is based on much less data than the X-Ray based networks, the networks are very similar. This similarity indicates that our graphs represent the essence of the movie. The biggest limitation in using X-Ray to generate movie social networks is that the full X-Ray dataset is not publicly available, and must be extracted manually.

There is no doubt that the presented method is not perfect. For instance, in the film Star Wars: Episode VI—Return of the Jedi (see Fig. 1 ), Princess Leia never meets Obi Wan Kenobi. Obi Wan Kenobi only talks with Luke about her, which created an edge in the graph. Nonetheless, from the network evaluation, we learn that the constructed networks represent the movie and have enough correct data to supply insights. Moreover, it is possible to perform many calibrations and parameter tunings to improve the method ’ s accuracy; for instance, we can manually select better subtitles to get more accurate networks. Such calibrations are out of the scope of this study, but in future studies we will explore such options.

Besides utilizing subtitles and screenplays, there are other possible ways to analyze movie content. The first option is to analyze movie videos as Weng et al. ( 2009 ) did. The problem with video analysis is that it is an expensive process which requires high computational power, especially when the plan is to analyze thousands of full-length movies. Moreover, most movies are copyrighted and not freely available online. The second option is to use speech recognition to extract information, which is what Park et al. ( 2012 ). However, this option has similar drawbacks.

Conclusions

Data science can provide great insights into many problems, including the gender gap in movies. In this work, we created a massive dataset of movie character interactions to present the largest-to-date SNA of gender disparities in the film industry. We constructed this dataset by fusing data from multiple sources, and then we analyzed the movie gender gap by examining multiple parameters over the past century.

Our results demonstrate that a gender gap remains in nearly all genres of the film industry. For instance, 3.5 times more relationship triangles in movies have a majority of men. In terms of top-10 most central movie roles, again there is a majority of men. However, we also saw an improvement in equality over the years. Today, women have more important movie roles than in the past, and our Bechdel test classifier quantifies this improvement over time by calculating a movie’s overall score. In a future study, we plan to analyze TV series, actors’ careers, and directors’ careers in a similar in-depth manner. We also plan to implement the tests that were proposed in (Walt et al., 2017 ) as well as develop new tests to gain further insight into how genders are represented in the film industry.

Data availability

The code and datasets generated during and analysed during the current study are available in the on the project’s website ( http://data4good.io/dataset.html#Movie-Dynamics ) and repository ( https://github.com/data4goodlab/subs2network ).

The Star Wars icons were created by Filipe de Carvalho and are licensed under CC BY-NC 4.0)

http://www.moviegalaxies.com

Many of the used movies’ subtitles were created by crowd-sourcing, i.e., by people who volunteered to create the subtitle.

American Beauty, Back to the Future, Back to the Future Part II, Funny People, Gladiator, Inglourious Basterds, Jurassic Park, Knight and Day, Marley & Me, Public Enemies, Serenity, Street Kings, Terminator 2 Judgment Day, The Godfather, The Godfather Part II.

https://www.imdb.com/

https://www.imdb.com/interfaces/

https://www.opensubtitles.org

https://github.com/Diaoul/subliminal

https://bechdeltest.com/ . Note the site uses the Bechdel test variation where women have to have names.

https://bechdeltest.com/view/3437/the_dark_knight_rises/

https://www.themoviedb.org

http://www.ise.bgu.ac.il/faculty/fire/computationalgenealogy/first_names.html

Similarly to Agarwal et al. ( 2015 ) this about 20%.

The bechdeltest.com data is mostly based on newer movies and there is too much noise in the graph for movies before 1965.

https://www.imsdb.com/

https://www.imsdb.com/scripts/Minority-Report.html

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Original research article, a neurocinematic study of the suspense effects in hitchcock's psycho.

• School of Arts and Communication, Beijing Normal University, Beijing, China

As a new and rapidly emerging cross-disciplinary research field, neurocinematics focuses on movie research from an empirical perspective, adopting functional magnetic resonance imaging (fMRI), and other cognitive neuroscience technologies as well as theoretical methods. By verifying and exploring relevant film concepts, neurocinematics tries to establish a scientific basis for the movie theory and better understand frontier subjects in movie studies. We designed this experiment to detect audiences' brain activity when watching movies and verify the manipulation power of narrative film. We selected the shower murder scene in Hitchcock's Psycho as the experimental material. The results of the experiment showed that the trends of the audiences' brain activity were almost consistent with the specific movie plots. By observing audiences' brain activity while watching the movie, the experiment verified the specific effect of Hitchcock's set-up of suspense and explored the neurocognitive brain mechanisms behind the suspense effect.

Media and cinema studies have always struggled to understand how cinema influences and manipulates human emotion ( Bondebjerg, 2014 ; Raz and Hendler, 2014 ). From the work of the gestalt analysis performed by Rudolf Arnheim to the development of Montage Theory by Sergei Eisenstein, and from the application of photography, editing, and other technologies to the technological revolution of 3D and virtual reality, all of these efforts have been made theoretically and practically ( Cutting et al., 2011 ). Based on the abundant existing research, more empirical, quantitative, and interdisciplinary evidence is currently required. When measuring viewers' perceptions of a movie, the box office is affected by various factors, and it cannot reflect the complicated feelings of the audience ( Kwak and Zhang, 2011 ). Behavioral measurements such as questionnaire surveys and interviews are adopted in empirical studies of cinema, which have become essential in these studies. However, inappropriate sampling, poor factor structure, or low internal consistency reliability may reduce the statistical reliability and validity, and subjects may claim more favorable behavior to please the interviewer or comply with accepted norms ( Hinkin, 1995 ; Chestnutt et al., 2004 ). Most of all, these methods lack the synchronous and dynamic capture of the audience's viewing psychology. The results are obtained after the film viewing activity had ended. Blood pressure, heart rate, and eye movement provide synchronous physiological data of movie-watching reactions to a certain extent. However, they are the external physiological manifestations of cognitive results and cannot explain the cognitive process or its mechanism in-depth when responding to movies. A deeper and more comprehensive understanding of movies' impact on audiences requires introducing new research methods.

Movies are multimodal, providing integrating audio-visual information, logical processes, and emotional experience. The process of humans watching movies is similar to the process of information processing in the daily environment. Understanding how the brain accepts and responds to movies will help understand how the brain perceives the real world ( Dudai, 2012 ). Since the beginning of the millennium, increasing numbers of cognitive neuroscientists have been using movie clips to study the human brain, and the interdisciplinary links between movies and the human brain have become increasingly close. Under the above circumstances, the interdisciplinary interactions between cognitive neuroscience and cinematography provide us with a suitable method ( Motz, 2013 ).

Cognitive neuroscience is a fundamental biological science that combines theories and experimental evidence from neuropsychology, neuroscience, and computational models. Functional magnetic resonance imaging (fMRI) measures neural activity with a high spatial resolution and is the only non-invasive and precision-oriented method for studying advanced brain functions. By measuring the blood oxygen response in subjects' brains while watching a movie, fMRI can accurately detect and locate the brain regions that generate activity during complex stimuli and determine which brain regions are called upon and to what extent ( Logothetis et al., 2001 ).

As a new and interdisciplinary field, neurocinematic studies mainly adopt empirical research, and experiment as common means. Researchers use fMRI technology and theories of cognitive neuroscience to perform empirical research on movies to verify and explore related topics in the field of film, to attempt to establish a scientific foundation for film theory, and to discuss the discipline's frontier topics in theory and practice ( Hasson et al., 2008a , b ). In recent years, the interdisciplinary cooperation between cinematic and cognitive neuroscience has gradually become a trendy field worldwide ( Dmochowski et al., 2014 ; Bondebjerg, 2017 ). However, there are still some gaps regarding specific interdisciplinary issues ( Stadler, 2018 ). A typical example is that cognitive neuroscientists pay more attention to brain structures and functions or psychotherapy than movie researchers, who show increased interest in movie audiences' reactions. The former approach treats movies as experimental materials for brain research, while the latter approach uses brain knowledge and technology to understand better movies themselves ( Willems et al., 2011 ; Plantinga, 2012 ; Bondebjerg, 2014 ; Francuz and Mendyk, 2014 ; Pehrs et al., 2014 ; Lahnakoski et al., 2017 ). Finally, the research team of Uri Hasson from the Weizmann Institute of Science in Israel brought the interdisciplinary vision into reality.

Hasson's team used a clip of the first 30 min of the famous film The Good, The Bad, and The Ugly as their experimental material. They recruited five participants to watch the clip in a functional MRI scanner without additional experimental tasks. Through naturalistic viewing, the participants' direct brain responses had been captured by the scanner. Compared to traditional fMRI experiments, which usually use single stimuli such as a single picture or word and ask subjects to do responding or rating tasks during the scanning process, Hasson's experimental designs replicated the audience's daily natural movie-watching mode to some extent, without any human intervention in the watching behavior. The experiment can be carried out under a continual and natural, therefore relatively ecological condition. Inter-subject correlation analysis (ISC) was a critical component of Hasson's research. By comparing the consistency of different subjects' brain activity when watching the same movie, the influence of movies on an audience can be generally and universally discussed. The results showed that while the five subjects watched the movie, they showed similar brain activities at a high level, suggesting that movies can control the human brain to some extent ( Hasson et al., 2004 ).

Hasson's research moved the combination of cinematics and cognitive neuroscience from theory into a period of actuation and development. Despite Hasson's research, empirical neurocinematic studies of particular problems of movies initiated by movie researchers are still rare at present. In the field of cinematics, to make movies attract larger audiences has always been a concern of movie-makers ( Rooney and Hennessy, 2013 ). At the same time, it is also one of the topics that movie researchers focus on ( Bordwell, 2010 ). According to narrative transportation theory, attractive narrations suppress audiences' perception of surroundings beyond the screen and make audiences more immersed in the story world, even take the story for real ( Gerrig, 1993 ). Suspense is one of the factors linked to increased transportation ( Tal-Or and Cohen, 2010 ). As a fundamental human emotion, suspense plays a pivotal role in attracting an audience's attention ( Cheong and Young, 2008 ; Lehne and Koelsch, 2015 ). On the one hand, it usually means some terrible events will happen, but on the other hand, a suspense moment is also the moment when terrible things have not happened yet. Therefore, when watching suspense movies, audiences hold strong emotions like fear, hate, or anxiety and need to activate complex cognitive mechanisms, such as prediction, anticipation, or moral judgment ( Comisky and Bryant, 1982 ; Naab and Sukalla, 2019 ). As to study audience involvement in movies, suspense is a suitable human emotion.

One of the definitions of suspense is anxiety arising from an uncerta in situ ation, and no one could produce that response in film audiences better than Alfred Hitchcock ( Adair, 2002 ). In world movie history, Hitchcock has been regarded as a master who can trigger fear inside of audiences. According to David Bordwell, Hitchcock's movies are some of the most suitable materials for film researchers to study since they enjoy certain accessibility through their stylistic and thematic obviousness to audiences ( Belton, 2003 ). Francois Truffaut, the famous French director, and film theorist, once commented on Hitchcock, saying that “he devoted himself to making the audience afraid. Let the audience find the feeling of childhood when playing hide-and-seek in a quiet house and hiding behind furniture to be caught” ( Truffaut, 1983 ).

Among Hitchcock's many works, Psycho broke taboos with its iconic shower-murder scene and its rejection of the norms of good taste ( Banash, 2015 ). In movie history, Psycho is also the unfading classic and has still been repeatedly analyzed or imitated to this day. Released in the 1960s, this movie was a huge success and the supreme audience-identification film ( Forbes, 1969 ; Thomson, 2009 ). Compared with horror movies that use blood, screams, and ghosts to scare the audience deliberately, Psycho created a psychological and personal terror that has left an indelible impression on generations of audiences and has also become a cultural fantasy about the overwhelming powers of cinema ( Banash, 2015 ). Therefore, the shower murder scene from Psycho is the ideal material for this experiment.

From the above, this paper draws on Hasson's team's method to study the brain activity consistency of subjects while watching Hitchcock's Psycho from the perspective of cinematics, and it preliminarily discusses the movie's suspense effect. On this basis, the movie and its suspense effect will be analyzed from a cognitive perspective. fMRI experiment and questionnaire survey will be combined to make these following hypothesis:

a. From a whole-brain perspective, the narrative, carefully structured suspense film clip has a synchronous and consistent effect on brain activity beyond multiple subjects. Compared with non-fiction or documentary movies, well-designed narrative movies have more robust control over the audiences.

b. From the functional brain regions' perspective, visual and auditory regions' brain activity characteristics may be related to the movie plot and maybe more active at critical plot points. Visual brain regions may be influenced by the design of movie images such as editing, the lens view, and the camera movement. The auditory brain may be influenced by the sound design in the movie, especially the music.

c. From the perspective of subjective memory and emotional arousal, the audience's memory and emotional response to the screenshot images may share the same characteristic to the brain activity in fMRI scanning. The performance of the memory and the emotion of the audience will be more active in the images of critical plots but less active in the images of transitional or unimportant plots.

Materials and Methods

The experimental material consists of two video clips, one of which is cut from the shower murder scene in Psycho . The video's length is 5 min and 42 s, with no dialogue except in the last shot. This video is the leading research object of this experiment. The starting and ending frames of the experimental video are shown in Figure 1 . The control video is a natural street scene outside the art school building of Beijing Normal University. Compared with the first video from Psycho , the second video does not have a specific plot or narration. It is a simulation of daily life, and the participants' viewing is closer to viewing in daily status. Both videos are in black and white. The aspect ratio and duration of the control video are the same as those in the clip of Psycho , as shown in Figure 2 .

Figure 1 . Beginning and ending shots of the Psycho clip.

Figure 2 . Clip of the street scene.

Participants and Procedures

Participants.

In total, 10 participants were recruited for the experiment, all of whom were college students aged between 21 and 25 years old. Five participants were male, and five participants were female, and all participants were right-handed and were not the movie or art majors. None of the participants had seen Psycho before, and they did not know the specific content of the experimental materials. All the participants have signed the informed consent forms before the experiment.

The fMRI Scanning Procedure

The study procedure consisted of two phases. In the first phase, fMRI was used to scan the subjects' brain activity while watching the movie and study the correlation of brain activity among the different subjects. During the experiment, the subjects laid flat in an fMRI scanner and viewed both video clips in a natural state. The videos were presented on a computer screen, and the sound was provided by headphones designed for the scanner. The subjects had no additional task requirements other than watching the videos. To eliminate errors caused by the sequence of the two video clips, we balanced the video viewing sequence among the subjects; five subjects watched the movie clip first and then watched the natural scene, while the other five watched the clips in the opposite order. During the whole process, the subjects' brain activities were scanned and recorded. After that, we calculated the ISC among ten subjects to obtain the average brain response during the watching process.

The fMRI experiment was conducted at the Institute of Brain and Cognitive Science of Beijing Normal University and the State Key Laboratory of Cognitive Neuroscience and Learning. It was supported by the review committee at the State Key Laboratory of Cognitive Neuroscience and Learning of Beijing Normal University.

Brain data acquisition was performed using a Siemens Trio Tim 3.0T scanner (Siemens Medical System, Erlangen, Germany) with a 12-channel phased-array head coil in the Imaging Center for Brain Research at the Beijing Normal University. The fMRI data were obtained using an echo-planar imaging sequence with the following parameters: repetition time (TR)/echo time (TE) = 2,000 ms/30 ms; flip angle (FA) = 90°; field of view (FOV) = 200 × 200 mm 2 ; matrix = 64 × 64; slices = 33; thickness = 3.5 mm; voxel size = 3.1 × 3.1 × 3.5 mm 3 ; gap = 0.7 mm; and 171 volumes. T1-weighted data were acquired using sagittal 3D magnetization prepared rapid gradient echo sequences. The sequence parameters were as follows: TR/TE = 2,530 ms/3.39 ms; FA = 7°; FOV = 256 × 256 mm 2 , matrix = 256 × 192; number of slices = 144; thickness = 1.3 mm; and voxel size = 1 × 1.3 × 1.3 mm 3 .

A series of preprocessing procedures were performed for the fMRI data to reduce non-neural noise by using Statistical Parametric Mapping software (SPM8; www.fil.ion.ucl.ac.uk/spm ) and the Data Processing Assistant for Resting-State fMRI (DPARSF) ( Yan and Zang, 2010 ), including slice time correction, head motion correction, a temporal band-pass filter (0.01–1.25 Hz), detrending, normalization to the Montreal Neurological Institute (MNI) space based on the T1 image, and spatial smoothing. Then, for each participant, the preprocessed time course of each voxel within a gray matter mask was extracted for the following analyses.

To quantitatively estimate subjects' involvement in the movie based on the brain fMRI data, we calculated the inter-subject correlation (ISC) for each voxel. Specifically, for a given voxel in the brain, we calculated Pearson's correlation coefficient between the time course of a subject and the averaged time course of all the other subjects. Overall, there were 45 unique pairwise comparisons between the 10 subjects watching the same clip. Then, we averaged the Pearson's correlation coefficient across 10 subjects to obtain a whole-brain activity consistency map for the movie and the natural condition. By calculating the ISC of 10 subjects, we can examine the intersubject dimension of film watching and determine whether the audience has a similar cognitive process.

The Behavioral Test Procedure

The second phase was a behavioral test that was a supplement to the first part of the brain imaging experiment. After the brain imaging scans were completed, the subjects entered another room to take an image recognition and behavioral test related to the video clips. The materials for the behavior test were programmed with E-Prime software and were presented on a computer screen. The subjects were asked to press a key to answer the questions. When randomly presented with an image, the subjects first judged whether they had seen it during the previous fMRI scan. If they had seen it, they pressed button 1; if had not seen it, they pressed button 0. Since the emotional activation is a key to audience empathy and has been used to measure cognitive effects of movie narrations ( Raz and Hendler, 2014 ; Gruskin et al., 2019 ), we then asked subjects to rate the emotional intensity caused by the current image on a 5-point scale, with “1” meaning no suspense feeling and “5” meaning a strong suspense feeling. The viewed images were from the video clip of Psycho shown in the previous fMRI scan. We captured one screenshot every 5 s. After removing duplicate images, 32 images were used for the test, as shown in Figure 3 . To ensure the homogeneity of the two groups of images, 16 images ( Figure 4 ) that were also screenshots from Psycho that the subjects had not seen during the previous fMRI scan were included. The subjects' memory and emotional response to previous experimental clips are measured to clarify the perception further through image recognition.

Figure 3 . The sample of video screenshots that were shown during fMRI scanning.

Figure 4 . The sample of video screenshots that were not shown during fMRI scanning.

Results and Discussion

Scanning data by fmri, consistent spatial distribution of brain activity.

By the fMRI scanning and the method of ISC, we obtained the average brain activity of subjects while watching the experiment clips. Figure 5 shows the consistent brain activity areas ( Figure 5A ) when the subjects watched the clip of Psycho and the consistent brain activity areas ( Figure 5B ) when the subjects watched the street scene ( r > 0.1). When watching the movie clips, brain activity areas were larger than those of the natural scenes. As seen from the figure, the activity in the brain's areas caused by the movie clips was more intense than those caused by the natural scene. Compared with the usual daily scene, a delicately constructed movie clip has generated a more consistent cognitive process pattern.

Figure 5. (A) Arousal in brain regions while watching Psycho clip. (B) Arousal in brain regions while watching street scene clip. The colors indicate areas of consistent brain activity and the closer the color to red, the higher the activity's consistency.

According to the brain scanning result in Figure 5A , the bilateral superior temporal cortex, lateral visual cortex, fusiform, and frontal eye fields were highly activated during the Psycho clip watching. These brain regions are involved in visual and auditory information processing and are related to face recognition and eye movement ( Gross and De Schonen, 1992 ; Owen et al., 1998 ; Grill-Spector and Sayres, 2008 ). However, during the street scene watching, those areas were little or not activated. This result suggests that fictional narration and construction of suspense lead audiences to a higher cognitive level and make them engaged in more positive perception. Audiences are more active in the aspect of visual, auditory processing, as well as attention and object recognition in movie watching than natural vision.

Changes in Brain Activity Signals Over Time

Figure 6 shows the brain activity signals of 10 subjects over time as they watched Psycho clip ( Figure 6A ). The horizontal axis is the timestamp of the experimental movie clips, and the vertical axis is the activity intensity of the significantly consistent areas; the larger the vertical axis value, the higher the activity intensity. Since the fMRI scanner works by scanning every 2 s, this experiment's scanning obtained 171 value points in all during Psycho clip watching process. The 10 thinner color curves in the figure show the brain activity of 10 subjects. The brain activity of 10 subjects shows a correlated trend during the movie-watching process. The bold red curve in the figure represents the average brain activity curve of the 10 subjects, shown in Figure 6B . According to the distribution of the high and low points of the curve in Figure 6B , the time stamp can be roughly divided into the following sections of durations of various lengths, and the corresponding narrative plots in the experimental video clip can be found.

Figure 6. (A) The whole-brain activity signals of ten participants while watching Psycho clip. (B) Average whole-brain activity signal of participants while watching Psycho clip.

Section A (point 1 to point 30, corresponding to 1–60 s), which includes the first high point and long duration, is the section with the most prolonged duration of high-intensity brain activity in the whole video clip, corresponding to the peeping in the movie clip. Norman's peeping makes the brain signals of the audience active, which is precisely what Hitchcock himself wanted to achieve. Joseph Stefano, the screenwriter of Psycho , recalled how Hitchcock enthused over the chance to manipulate sound to heighten audience involvement and to implicate them, with Norman Bates, as voyeurs. In the screenplay, Stefano wrote under the direction of Hitchcock: “The SOUNDS come louder as if we too had our ears pressed against the wall” ( Rebello, 2004 ). Meanwhile, no one will ever forget that huge eye in the close-up, which is a disconcerting mixture of desire and intrigue.

Section B (point 31 to point 89, corresponding to 1–2 min and 58 s) contains a series of high points, each of which has a short duration but roughly equal intervals. The fluctuation of the curve is basically at the same level. According to the narration storyline, section B is the passage from the end of peeping to the beginning of shower murder. It is a transitional paragraph in the whole clip's narration; audiences' brain activity shows a random, irregular, and free trend. Besides, the visual and auditory design in this paragraph also has a foreshadowing characteristic. We have seen a lot of moving lens which contains information of surroundings instead of one specific character or object. The soundtrack for this section is also dominated by low and blunt bass, creating an atmosphere of uncertainty. Just like the word of Hitchcock himself, “the essential fact is, to get real suspense, you must have information” ( Adair, 2002 ). Brain signals in Section B indicate that the audiences are invoking cognition to process the information, to understand and predict what will happen next.

Section C (point 90 to point 129, corresponding to 2 min and 58 s to ~4 min and 18 s). This section has the highest activity intensity in the whole clip and has reached to the peak intensity (1.2896). This section corresponds to the famous shower murder scene, and this is both stunningly unexpected and a logical release of the pressures built up in the long, sustained overture, and also is the climax of this clip. As a physiological and psychological result, the brain's signal intensity increased with Marion's sudden terrible scream and the simultaneous sound of a violin, the knife plunged at the poor girl, and the signal intensity got to the top of this clip at the same time. This signal movement indicates the power of murder. Shown in quick cuts and a terrific soundtrack, the bathroom is hell.

Section D (point 130 to the final point, corresponding to 4 min and 18 s to the end of the clip). Compared with the first three sections, several high-strength points and curve trends in this section do not show apparent characteristics. This section corresponds to the death of Marion and the epilog of this killing. During this section, the brain signals got weaker than the climax, but they still showed up and down changes and fluctuations. This suggests that the audiences' brain is actively processing new information, despite having just witnessed an unexpected murder and experienced severe cognitive and psychological shock. For instance, as the camera moves from the bathroom to the bedroom, the audience will see something wrapped in the newspaper next to the bed. What is that? What is that for? Does this have anything to do with the murder? All the questions are waited to be answered. The brain is going to be activated.

In summary, the brain activity of the subjects has obvious ups and downs during the whole movie-watching process, and these ups and downs correspond to different stages of plot development in the film's content, in line with Hitchcock's expectations of the effect of the movie. By the method of ISC, we found that audiences' reactions can be manipulated by the constructed narrative and audio-visual design of the movie. This consistency in brain activity is also evidence that film can reach a resonance between different people.

Activity of Brain Regions Associated With Visual and Auditory Functions

Visual brain activity.

After discussing the whole brain regional data, we extracted the brain signals from visual and auditory regions, including the bilateral superior temporal cortex, lateral visual cortex, fusiform, and frontal eye fields. As shown in Figure 7 , in terms of spatial arousal of brain activity, there was arousal in all of these brain regions, indicating that in the process of primary audio-visual processing, the film has an obvious impact on the subjects. According to the usual way of grouping high and low values in statistics, data of brain activity signals were grouped into high and low values. The mean of the high-value visual activity group is 0.7428, and the mean of the low-value visual activity group is −0.7798. The mean of the high-value auditory activity group is 0.7267, and which of the low-value auditory group is −0.7537. The independent sample T -test was performed. As in Figure 8 , the result showed that p < 0.05, indicating that there were significant differences between the high-value and the low-value group both in the visual and auditory brain activities.

Figure 7 . The arousal condition of visual and auditory brain regions during Psycho clip watching.

Figure 8 . Statistical results of data from visual and auditory brain regions while watching Psycho clip. We have arranged all values from the largest to the smallest, according to the convention of statistical analysis, the high-value group is of the first 27%, and the low-value group is after 72%.

Figure 9 shows the time intervals with high and low visual activity values and the corresponding images. The plot contents corresponding to the time intervals with high visual activity values are Norman's peeping, calculation of money, flushing paper scraps in the toilet, shower murder, dead eye in a close-up, and the newspaper by the bed. The plot contents corresponding to the time intervals with low visual activity values are Norman's pondering, Norman walking through the corridor, Norman sitting in the living room, Marion taking a shower, Marion falling after the murder, and Marion's dead eye in a zoom-out.

Figure 9. (A) Average activity signals of visual brain regions over time. (B) Time intervals with high and low visual activity values and corresponding images.

The visual brain activity signal values are high when important events happened, such as the murder and peeping, as well as when key objects, such as the toilet bowl and the newspaper, are highlighted in the picture. In transitional scenes, such as when Norman is walking through the corridor or Marion is taking a shower, the visual brain activity values of the subjects are at a low level. The following discussions can be performed after taking the design of editing, scenes, and camera movements into consideration.

First, from the perspective of editing, the rapidly edited shots all led to a higher level of visual activity. As shown in Figure 9 , the peak value group of the whole curve, between 3′40′′ and 3′50′′, is at the climax of the shower murder. In total, 16 shots are used in just 10 s, and the average length of each shot is <1 s. High-speed, dense, and coherent editing keeps the visual brain areas highly excited as they receive rich visual stimulation from the film.

Second, from the perspective of the lens view, the visual brain regions are more active with close-up shots than in a wide scene. For example, the close-up shots of Norman's peeping eye (0′32′′), the toilet bowl (2′28′′), Marion's dead eye (4′50′′), and the newspaper by the bed (5′24′′), all belong to the high-value group of activity signals and have stimulated higher activity signals than the other mid, panoramic, or long-range shots in the low-value group. Compared with the smaller scenes' visual simplicity and object prominence, larger scenes contain much information that distracts audiences' attention. For example, a series of shots depicting Norman walking from the hotel to the villa consists of the medium, panoramic and distant views, and the visual activity of the subjects is in a low state.

Finally, from the perspective of camera movement, the moving lens covering important narrative information triggered high-value activity in the visual brain regions. For example, at the end of the shower murder scene, the camera zooms out from a close-up of Marion's eye, pans right to the drawing, through the bathroom, and to the newspaper on the bedside table. At this point, the visual regions' activity increased from the previous low value (−0.4139) to a high value and reached a small peak (0.8155). The movement of the camera means a change in information. If the camera is the privileged perspective of the audience, then the moving camera will lead the audience to move with it, mobilize their attention, and approach or exit a scene or detail. In suspense films, the audiences' emotions are highly aroused, and at the same time, more advanced and deeper cognitive activities are also carried out in guessing and solving the mystery. Driven by this sensitivity, the moving lenses bring new clues, and its arrangement of the image is, to some extent, controlling the direction of the attention and thinking of the audience.

It is said that Hitchcock completed the principal photography of Psycho in 45 days. Of all the scenes in the film, the one on which he spent the most time was the shower murder. It took him an entire week. The scene lasted <1 min, but nearly 50 lenses were used, and the camera shifted more than 60 times. Those details of the visual design may not be recognizable, but the results have indicated that the visual brain region of the audiences has clearly and positively received and recognized the information. Therefore, although the knife never actually stabbed the actress, the film's intense visual movements created a vivid sense of violence that made audiences feel as if they were being stabbed in fear and despair as well. Regarding the visual effects of a film, Hitchcock himself once said, “The action must be divided into several details, and then jump from one detail to another. If the camera is always in the same place, acting only as of the recorder, the film cannot control the audience.” The results of this experiment also indicated that he successfully put this idea into practice. Through the editing, lens views, and camera movement, the film successfully mobilizes and affects the audience's attention and psychological state.

It is worth pointing out that although the visual design characteristics of Psycho corresponded to several visual activity signal value groups, the whole activity in the visual brain regions was still unstable and variable. Human beings mainly rely on a visual sense of living. To adapt to the complex and diverse ecological environment, the visual brain regions are extremely flexible to receive, process, and respond to numerous stimuli in daily life. They can quickly reach an excited state when facing visual stimulation. At the same time, this excited state is not easy to maintain for a long time and will rapidly decline. Hitchcock's visual design of Psycho has successfully caused several ups and downs of the visual brain activity in a short time and has seized the visual attention of the audiences. However, at the same time, its impact on the audience's cognition is not only realized by visual design. The sound design is also an important factor in making Psycho a classic, especially in the shower murder scene.

Auditory Brain Activities

The average activity in the auditory brain regions of the 10 subjects is shown in Figure 10 . We corresponded the activity signal data to the film plot, and the plots which high-value groups correspond to were the shower murder (3′34′′-4′02′′), Norman's peeping (0′26′′-0′42′′), Marion tearing the paper (2′04′′-2′10′′), Marion flushing the toilet (2′20′′-2′32′′), and blood flowing into the sink or bathtub (4′42′′-5′02′′). In the climax scene or important clue scenes, the activity of auditory brain regions was activated, and the signal values were higher than average. This indicates that the audiences were led and influenced by the design of the sound in the movie.

Figure 10. (A) Average activity signals of auditory brain regions over time. (B) Time intervals with high and low auditory activity values and corresponding images.

The highest value (1.806) of auditory activity is in the classical time interval when the shrieking violin and Marion's scream almost together burst. Before that, the main sound was from Marion's relaxing shower. It was peaceful and calm. The girl finally decided to give the money back and to end all the chaos she caused. Things seemed to be getting better, and audiences also felt relaxed. The signal values at this point were in the low-value group, both in Figures 9 , 10 . At the moment of the mystery shadow's appearance, the sound environment suddenly switched from natural to musical, and signal values in the auditory brain region rose rapidly to the peak just in 10 s (from −0.596 to 1.806). This transition from ambient sound to the designed soundtrack is more conducive to the quick transition from a relaxed state to a highly aroused state in the auditory brain regions and also formed the climax from the auditory aspect.

Seeing the horrible shadow and the knife, Marion screamed. Her scream is a combination of fear and shock, which actually can be predicted by the audiences who have experienced all these events along with her. The actual scream was the music, the shrill sound of violins, which nobody could foresee. Its playing was so abrupt that it terrified everyone who just heard it. Cutting through the previous atmosphere of silence, it was the music that made the murder more terrifying. Bernard Herrmann, the composer of Psycho , deliberately did not add any music in the scenes just before the murder and created an atmosphere of relaxation, thus made the music more sharp and terrible. After the murder, Hitchcock chose to cut the music and slowly amplify the natural sound of running water, making the bathroom seem to be in silence. This silence beneath the natural sounds marked the end of the murder, making it full of desperation. As we can see in Figure 10 , the time interval corresponding to the flowing water (4′42′′-5′02′′) is in the auditory signal's high-value group. The effect of this sound design is demonstrated by auditory brain activity.

In the history of film, Psycho is a unique case of music saving a whole movie. After seeing Psycho 's rough cut, Hitchcock was so depressed that he began to think about cutting the movie and using it for television. The devastating score made him rethink this decision after Herrmann wrote the murder cue against his wishes. As he stated, “The basis of the cinema's appeal is emotional. Music's appeal is to a great extent emotional, too. To neglect music, I think, is to surrender, willfully or not, a chance to progress in filmmaking.” At the same time, the shower cue by Herrmann is also of great significance in the cultural dimension. In the 1960s, the sound of violins ripping through the silence not only brought the horror of Psycho to a climax but also implied that there was no safe harbor anymore in daily life, even if a warm and cozy bathroom was fraught with peril. It was indeed the cultural anxiety constructed through film sound.

Behavior Test Reporting Data

After the fMRI scanning, all the subjects have been asked to do the image recognition and behavior test about the memory accuracy and the emotional arousal. Following the steps stated in the method section, we have collected and calculated the reporting data. As stated in the method section, the subjects pressed button 1 if they had seen the image on the computer screen during the previous fMRI scanner and pressed button 0 if they had not. After that, they rated each image on a scale of 1–5 according to the suspense feeling they get from the image. So, in the memory accuracy test, each image had an average memory recognition score. According to the accuracy score of memory, Figure 11 has been generated. Of the screenshots of all the clips, images with the highest memory accuracy are shown in Figure 11A . Those with the lowest accuracy are shown in Figure 11B . The subjects remembered key plot points better than general plot points. For the screenshots of the scenes that included peeping, calculating the amount of money, and the appearance of the shadow, the subjects' recognition accuracy was 100%, completely consistent with the brain imaging results in the first stage of setting up the suspense, the second stage of information capture and the third stage of the climax, respectively. The close-ups of the toilet, the showerhead, the sink of the bathtub, and other images that contain important clues and are conducive to reasoning and the judgment of the plot also had 100% recognition accuracy. It indicated that the subjects formed a deeper memory of key plot points than normal or transitional plot points. From the perspective of emotion, as shown in Figure 12 , the emotional rating of the subjects of the key plot points was higher than that of the general plot or transitional plot. The subjects gave high emotional scores to the suspenseful, visually stimulating close-up shots than normal transitional shots. The audience has perceived the film's audio-visual design, and the correct cognitive feedback has been formed. These results are consistent with those obtained in the fMRI experiments, which confirm that movies influence human brain cognitive activities.

Figure 11. (A) Images with 100% accuracy in the memory test. (B) Images with the lowest memory accuracy scores. Statistical significance tests have been performed on the high-value and low-value data, and p < 0.05.

Figure 12. (A) Images with high emotional rating scores. (B) Images with low emotional rating scores. Statistical significance tests have been performed on the high-value and low-value data, and p < 0.05.

However, there are also inconsistencies between the fMRI results and the results from the memory and emotion tests, which are mainly reflected in the following two aspects:

First, there was a difference in the audience's reaction to Marion's dead eye's close-up. In the memory and emotion ratings, this group of close-up shots had a high recognition accuracy and a high emotion score, but in the brain imaging data when these close-up shots appeared, the audience did not show a high intensity of brain activity, which may be different from our conclusions based on experience. It did not indicate a decline in the appeal of the film. The memory and emotion test results proved that these shots have left an impression on the audience and attracted the audience's attention. The brain-imaging data's performance might be due to the lack of cognitive engagement of the audience at the time, rather than the lack of impression or emotional enrollment. When the dangerous event in the clip was over, the audience felt relaxed and started to think about who the murderer was. Then, the audience entered into a more advanced state of thinking. In this stage, the ISC of brain activity would be reduced due to each person's different thinking modes and experience backgrounds.

Second, being the most suspenseful scene in the whole clip, the peeping scenes got high scores in brain imaging and memory accuracy. However, the emotional score was not in the high-value group, which became another contradiction.

From an experimental point of view, it could result from the participants having seen all the shots and therefore stating this was low compared to the murder shots. It could also be because the peeping scene appeared earlier in the clip when the audience felt that they had not fully entered the narration and therefore, that their emotions had not been activated. No matter what, this inconsistency, however, was what Hitchcock wanted. He talked about fear and suspense in films in an interview: “This is my specialty, I will divide it into two categories – fear and suspense. Surprise leads to fear, suspense comes from anticipation.” This suspense becomes purer and more attractive after removing the influence of emotions such as fear.

Conclusions and Outlooks

Through an fMRI scanning and behavior test, the study verified that the suspenseful movie has a stronger influence on the audience's mind than the daily non-narrative unstructured video. Additionally, this experiment has also analyzed the suspense effects of the classic shower murder in Psycho from a cognitive perspective via an empirical analysis and an audio-visual analysis. The results showed that during the process of watching the film, the average brain activity of the subjects was influenced by the content of the film, activity in the visual and auditory brain regions was also influenced by the characteristics of the visual and the soundtrack designs. The audience showed active brain cognitive activities and positive memory and emotional engagement to close-ups, quick editings, critical visual information, and dissonant music. The shower murder of Psycho is a classic that can hardly be surpassed or imitated in the history of cinema, but its success is accomplished in the most basic way: to get close and activate the audience's cognition process.

This study is a preliminary attempt to combine film study with cognitive neuroscience, especially in the aspect of the method. This study not only introduces fMRI, a technology rarely used in film studies before, but also adopts the cognitivism paradigm. Since the middle of the 1980s, cognitivism, or Post Theory, which advocated by Noël Carroll, David Bordwell, Murray Smith, Stephen Prince and others, has become a new voice in film research. They questioned the traditional film research methodology called the Grand Theory by Bordwell, and criticized its excessive structuralism tradition, which made film research driven by theories rather than specific issues. At the same time, traditional research methods are also addicted to interpreting the film content, avoiding reasoning, and logical argumentation. Most of all, the audience's psychological activities are classified into the unconscious and lose the initiative. Stephen Prince used the phrase “the missing spectator” to refer to the absence of the audience's perspective in film study. He believed that psychoanalysis's traditional film theory ignored the important role of the perceptual process, which is one of the central issues concerned by the cognitive theory. Noël Carroll and David Bordwell put forward “piecemeal theory” and “middle-level issue,” respectively, emphasizing that film research should focus on specific issues and carry out more research and theoretical exploration, which are performed bottom-up. Although cognitivism does not emphasize that it must be studied empirically, this study is a practice and enrichment of cognitivism. It has been a cliche that Psycho is engrossing and owns millions of fans. Rather than analyzing the techniques used in the film or reading the creators' interviews repeatedly, it is better to switch perspectives and methods, to explore what specific cognitive processes and emotional experiences the audience had while watching this film.

Regarding specific methods and operations, simultaneous cognitive activities were obtained by fMRI scanning during the watching process. Compared with the traditional effect research based on questionnaires and interviews, the results of brain activity signals were not obtained after the movie watching but were obtained simultaneously with the watching behavior. This method of film effects measuring has the potential to be used in more future areas such as pre-testing of commercial films or film classification. Through this new approach, deeper and more precise answers from the audience will come to the stage.

It should be noted that in this experiment, the division of subsections of movie clips is not accurate. Due to film information transmission and reception's complexity, the audience's response presents a continuous state. We established the starting and ending points of each section according to the changes in the data. However, in the human brain's complex cognitive process, strict starting and ending points do not truly exist. In future studies, we will adopt behavioral experiments, eye movement measurements, and other means to improve these deficiencies.

This study was carried out in a laboratory environment that was independent, closed, and non-social. Movies are often simultaneously watched by crowds. There will inevitably be some contradictions between the two environments. Limited by the existing experimental conditions, it is difficult to perform undetectable collective brain scanning at present. Research on the combination of film and neuroscience still has a long way to go.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Institutional Review Board of the State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author Contributions

YaW was responsible for data processing and article writing, while YiW was responsible for experimental design, article modification, and proofreading. All authors contributed to the article and approved the submitted version.

This study was financially sponsored through Beijing Municipal Philosophy and Social Science Planning Office (Grant No. 16YTA002) and National Social Science Fund of Art (Grant No. 19BC041). The authors thank them for financial support.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Professor Yong He and Dr. Jin Liu of IDG/Mc GOVERN INSTITUE—BNU for their help in data collection processing and analysis. As an interdisciplinary research study, this paper benefits a lot from IDG/Mc GOVERN INSTITUE—BNU. The authors would like to thank the following people for their assistance in the early stage, including materials preparing, data collecting, and participants recruitment. They are Zhichen Shi, Quanquan Wang, Wanyi Zhang, and Xiaoyang Li.

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Keywords: neurocinematics, fMRI, suspense, Hitchcock, brain

Citation: Wang Y and Wang Y (2020) A Neurocinematic Study of the Suspense Effects in Hitchcock's Psycho . Front. Commun. 5:576840. doi: 10.3389/fcomm.2020.576840

Received: 27 June 2020; Accepted: 26 October 2020; Published: 12 November 2020.

Reviewed by:

Copyright © 2020 Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yiwen Wang, wyiwbnu@126.com

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Subject Guide

Film and television studies at American University

If you’re new to the American University Library’s website and have not used our resources much, this is a guide to the resources that should be considered when getting started with film studies research. Brief descriptions of the results of a sample search in various databases are used to illuminate the breadth and depth of the databases listed.

The filmmaker Atom Egoyan is the example used partly because his name is distinctive and doesn’t yield too many false hits and secondly his work isn’t mainstream or what one might consider popular so search results weren’t expected to be unmanageably large. The results should be substantial enough to indicate the strengths of some of the databases available to the AU community and encourage individuals researching lesser known films and filmmakers.

Atom Egoyan (1960-) was born in Cairo, raised in Canada,  and is an ethnic Armenian. He began making films in the early 1980s and is probably best known for   Exotica (1994), The Sweet Hereafter   (1997), and   Ararat   (2002). His films often deal with the themes of isolation, alienation, and loss. He has won many international film awards and has been the subject of considerable critical attention.  

In the sample searches the keywords “atom” and “egoyan” were searched together but not as a phrase – in order to capture all incidences of his name including subject headings that placed his last name first and articles that may have included a middle name or initial.

NOTE : Unless noted, remote access to these databases, such as from your home or office, is restricted to American University students, faculty, and staff. For a full list of our ALADIN databases (with descriptive notes), go to the Databases link on our homepage. Also note some of the databases do not contain full-text. For assistance in locating articles found in the citation-only databases, please contact the AU Library Reference Desk or call (202/885-3238).

Don't forget print The list is composed only of electronic resources, but thorough film studies research still requires extensive use of print resources as well. For a guide to some of the standard film studies reference books available at the AU Library go to our  Film and Television Studies Print Reference Guides .

• Arts & Humanities Citation Index This link opens in a new window Mostly interviews with the filmmaker.
• Encyclopedia Brittanica Online References to Egoyan in the context of Canadian cinema and in the Performing Arts sections of various Book of the Year editions.
• FIAF International Film Archive This link opens in a new window Results from a broad international collection of film periodicals. A high percentage of articles are written in languages other than English. The database cites articles from a variety of scholarly, industry and aficionado journals on film. It contains no full-text of articles.
• Google Scholar This feature of the ubiquitous search engine claims to be the definitive way to search for scholarly articles on the web. Results vary wildly from scholarly articles to cryptic web pages. Results are ranked by the number of times the works are cited in other scholarly publications. When Google Scholar is used from an AU affiliated computer, search results will include a search in catalog link to find consortium owned books or will include a Full text at AU link if an article is available in an AU database. Editors choice.
• Internet Movie Database (IMDB) This free database contains biographical information and a thorough filmography of works for which Egoyan has written, directed, produced, edited, and/or acted. Each film has its own entry. Entries include a wide variety of information including credits, filming locations, box office, trivia, and user comments. Editors choice.
• Movie Review Query Engine This free database with links to full-text movie reviews must be searched by individual title. Editors choice.
• PapersFirst (Worldwide Conference Papers) This link opens in a new window This is a database with partial full-text coverage. It’s not strong in film studies subjects.
• Periodicals Index Online (PIO) This link opens in a new window This article index focuses on thirty-seven subject headings in the humanities. Film Studies are lumped into Performing Arts. Since PIO is just an index, it doesn’t contain full-text articles but does provide links to full-text in other databases when available. There is overlap with other full-text databases on this list so for film studies research this need only be consulted in the course of an exhaustive literature search. Periodicals Archive Online does provide full-text articles and includes one relatively thorough review of a book about Egoyan.
• Proquest Research Library At the bottom of the search screen, select "magazine"
• VideoHound's Golden Movie Retriever An online e-book of the print movie guide. VideoHound provides very brief information about films such as cast, crew and plot summary.
• WorldCat (OCLC FirstSearch) This link opens in a new window Primarily book citations (including some in French, Spanish, German, and Italian), and film/video citations. Note: Worldcat is a union catalog of holdings from libraries around the world and since a given title may have been released in different editions or different formats, it’s not unusual to find multiple (and sometimes many) records for the same title.
• Next: Film/Television Studies Databases >>
• Last Updated: Mar 19, 2024 2:02 PM
• URL: https://subjectguides.library.american.edu/c.php?g=175106

Film & Media Studies Resources: Researching a Film

• Researching a Film

Types of Film Analysis

• Finding Books
• Journals & Articles
• Finding Films

Watching Film Analytically

1. start with the assignment..

Review the assignment prompt and identify the tasks your instructor has asked you to perform and the questions you've been asked to address. Write them out at the top of your notes before watching the film.

2. Review film terms.

Review the terms you've learned in class and practice applying them while watching your film. Studying these terms before you begin watching can help you develop abbreviations and avoid searching for these words while you watch.

3. Watch the film.

Watch the film at least once without once before (unless you've seen it before) to watch uninterrupted. When you take notes, be sure to pause when writing. This disrupts the viewing experience. 

Starting the Film Analysis Essay

4. brainstorm.

After you've watched the film at least twice, it's a good idea to brainstorm ideas based on the notes you took. Cluster your ideas around the themes or topics that emerge in your notes, possible in a concept map. If you're writing an argumentative essay, your brainstorming ideas can be used to draft your thesis statement or research question.

Things to remember:

• Use your assignment prompt as a guide.
• Write about the film in the present tense in your essay. (i.e., “In  Vertigo , Hitchcock employs techniques of observation to dramatize the act of detection.”)

5. Make a research plan.

• Review your brainstorming notes and decide what type of analysis you want to write.
• Do you need research or other background information for your essay?
• Do your sources need to be scholarly or can you use critics' review?

6. Find Sources and Reviews

• Finding a screenplay/script of the movie may be helpful and save you time when compiling citations. But keep in mind that there may be differences between the screenplay and the actual product (and these differences might be a topic of discussion!). The Popular Culture Library has a great collection of movie scripts. 
• Reading reviews and other analysis essays between viewings can help your own analysis of the film.  Search in Summon or subject databases listed below for the film's title and the ideas you brainstormed to look for sources.

Symbolic Analysis

Symbolic (or semiotic) analysis is the interpretation of signs and symbols, usually involving metaphors and analogies to both inanimate objects and characters in a film. Because symbols can have multiple meanings, you will need to determine what a particular symbol means both in the film and in a broader context, whether in other films, or in other disciplines, like literature. 

Be sure to bring the analysis back to your thesis, or why this symbolism matters.

Some questions you could ask when writing a symbolic analysis essay:

• What images or objects are repeated in the film?
• What colors, clothing, or food is associated with a character?
• How does a symbol or object relate to other symbols and objects?

Narrative Analysis

Narrative analysis is an examination of the narrative structure, character, and plot of a film (i.e., the story elements). This analysis considers the story the film seeks to tell. 

Questions to consider when writing a narrative analysis:

• How does the film fit into the Three Act structure?
• How does the plot differ from the narrative of film? Or, how is the story told? (i.e., Are events presented out of order or chronologically?)
• Does the plot revolve around one character or multiple? How do these characters develop across the film?

Cultural or Historical Analysis

In this type of analytical essay, you examine a film's relationship to its broader cultural, historical, theoretical contexts. Sometimes films intentionally comment on these contexts, but even if they don't, they are still a product of the culture or time in which they were created. This type of analysis asks how the film models, challenges, or subverts these relationships.

Questions to ask for a cultural or historical analysis:

• How does the film comment on, reinforce, or critique social and/or political issues at the time it was released, including questions of race, ethnicity, gender, and sexuality?
• How might a biographical understanding of the film's creators and/or screenwriters and their historical moment affect the way the film is viewed?
• How might a specific theory, such as Queer Theory, Structuralist or Marxist Film Theory, provide a way of analyzing or viewing the film?

Mise-en-scene Analysis

A mise-en-scene (French for "putting on stage") analysis looks at the compositional elements of a specific scene or even a single shot, as well as the how those elements come together to produce meaning. You can focus on anything in the scene, including blocking, lighting, design, color, costume, and how these work in conjunction with other elements, like sound, cinematography and editing.

Questions to ask when analyzing a scene:

• What effects are created in a scene and what is their purpose?
• How does this scene represent the theme of the movie?
• How does a scene work to express a broader point to the film's point?

More Links of Interest

• BGSU Department of Popular Culture
• BGSU Department of Theatre and Film
• BGSU American Culture Studies Program
• Film Resources in the BPCL

Your Film Librarian

Film Analysis Books

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This guide was adapted from The University of North Carolina at Chapel Hill's Writing Center's Film Analysis and Watching Film Analytically .

• Next: Types of Film Analysis >>
• Last Updated: Nov 20, 2023 9:08 AM
• URL: https://libguides.bgsu.edu/film

Film Analysis

What this handout is about.

This handout introduces film analysis and and offers strategies and resources for approaching film analysis assignments.

Writing the film analysis essay

Writing a film analysis requires you to consider the composition of the film—the individual parts and choices made that come together to create the finished piece. Film analysis goes beyond the analysis of the film as literature to include camera angles, lighting, set design, sound elements, costume choices, editing, etc. in making an argument. The first step to analyzing the film is to watch it with a plan.

Watching the film

First it’s important to watch the film carefully with a critical eye. Consider why you’ve been assigned to watch a film and write an analysis. How does this activity fit into the course? Why have you been assigned this particular film? What are you looking for in connection to the course content? Let’s practice with this clip from Alfred Hitchcock’s Vertigo (1958). Here are some tips on how to watch the clip critically, just as you would an entire film:

• Give the clip your undivided attention at least once. Pay close attention to details and make observations that might start leading to bigger questions.
• Watch the clip a second time. For this viewing, you will want to focus specifically on those elements of film analysis that your class has focused on, so review your course notes. For example, from whose perspective is this clip shot? What choices help convey that perspective? What is the overall tone, theme, or effect of this clip?
• Take notes while you watch for the second time. Notes will help you keep track of what you noticed and when, if you include timestamps in your notes. Timestamps are vital for citing scenes from a film!

For more information on watching a film, check out the Learning Center’s handout on watching film analytically . For more resources on researching film, including glossaries of film terms, see UNC Library’s research guide on film & cinema .

Brainstorming ideas

Once you’ve watched the film twice, it’s time to brainstorm some ideas based on your notes. Brainstorming is a major step that helps develop and explore ideas. As you brainstorm, you may want to cluster your ideas around central topics or themes that emerge as you review your notes. Did you ask several questions about color? Were you curious about repeated images? Perhaps these are directions you can pursue.

If you’re writing an argumentative essay, you can use the connections that you develop while brainstorming to draft a thesis statement . Consider the assignment and prompt when formulating a thesis, as well as what kind of evidence you will present to support your claims. Your evidence could be dialogue, sound edits, cinematography decisions, etc. Much of how you make these decisions will depend on the type of film analysis you are conducting, an important decision covered in the next section.

After brainstorming, you can draft an outline of your film analysis using the same strategies that you would for other writing assignments. Here are a few more tips to keep in mind as you prepare for this stage of the assignment:

• Make sure you understand the prompt and what you are being asked to do. Remember that this is ultimately an assignment, so your thesis should answer what the prompt asks. Check with your professor if you are unsure.
• In most cases, the director’s name is used to talk about the film as a whole, for instance, “Alfred Hitchcock’s Vertigo .” However, some writers may want to include the names of other persons who helped to create the film, including the actors, the cinematographer, and the sound editor, among others.
• When describing a sequence in a film, use the literary present. An example could be, “In Vertigo , Hitchcock employs techniques of observation to dramatize the act of detection.”
• Finding a screenplay/script of the movie may be helpful and save you time when compiling citations. But keep in mind that there may be differences between the screenplay and the actual product (and these differences might be a topic of discussion!).
• Go beyond describing basic film elements by articulating the significance of these elements in support of your particular position. For example, you may have an interpretation of the striking color green in Vertigo , but you would only mention this if it was relevant to your argument. For more help on using evidence effectively, see the section on “using evidence” in our evidence handout .

Also be sure to avoid confusing the terms shot, scene, and sequence. Remember, a shot ends every time the camera cuts; a scene can be composed of several related shots; and a sequence is a set of related scenes.

Different types of film analysis

As you consider your notes, outline, and general thesis about a film, the majority of your assignment will depend on what type of film analysis you are conducting. This section explores some of the different types of film analyses you may have been assigned to write.

Semiotic analysis

Semiotic analysis is the interpretation of signs and symbols, typically involving metaphors and analogies to both inanimate objects and characters within a film. Because symbols have several meanings, writers often need to determine what a particular symbol means in the film and in a broader cultural or historical context.

For instance, a writer could explore the symbolism of the flowers in Vertigo by connecting the images of them falling apart to the vulnerability of the heroine.

Here are a few other questions to consider for this type of analysis:

• What objects or images are repeated throughout the film?
• How does the director associate a character with small signs, such as certain colors, clothing, food, or language use?
• How does a symbol or object relate to other symbols and objects, that is, what is the relationship between the film’s signs?

Many films are rich with symbolism, and it can be easy to get lost in the details. Remember to bring a semiotic analysis back around to answering the question “So what?” in your thesis.

Narrative analysis

Narrative analysis is an examination of the story elements, including narrative structure, character, and plot. This type of analysis considers the entirety of the film and the story it seeks to tell.

For example, you could take the same object from the previous example—the flowers—which meant one thing in a semiotic analysis, and ask instead about their narrative role. That is, you might analyze how Hitchcock introduces the flowers at the beginning of the film in order to return to them later to draw out the completion of the heroine’s character arc.

To create this type of analysis, you could consider questions like:

• How does the film correspond to the Three-Act Structure: Act One: Setup; Act Two: Confrontation; and Act Three: Resolution?
• What is the plot of the film? How does this plot differ from the narrative, that is, how the story is told? For example, are events presented out of order and to what effect?
• Does the plot revolve around one character? Does the plot revolve around multiple characters? How do these characters develop across the film?

When writing a narrative analysis, take care not to spend too time on summarizing at the expense of your argument. See our handout on summarizing for more tips on making summary serve analysis.

Cultural/historical analysis

One of the most common types of analysis is the examination of a film’s relationship to its broader cultural, historical, or theoretical contexts. Whether films intentionally comment on their context or not, they are always a product of the culture or period in which they were created. By placing the film in a particular context, this type of analysis asks how the film models, challenges, or subverts different types of relations, whether historical, social, or even theoretical.

For example, the clip from Vertigo depicts a man observing a woman without her knowing it. You could examine how this aspect of the film addresses a midcentury social concern about observation, such as the sexual policing of women, or a political one, such as Cold War-era McCarthyism.

A few of the many questions you could ask in this vein include:

• How does the film comment on, reinforce, or even critique social and political issues at the time it was released, including questions of race, ethnicity, gender, and sexuality?
• How might a biographical understanding of the film’s creators and their historical moment affect the way you view the film?
• How might a specific film theory, such as Queer Theory, Structuralist Theory, or Marxist Film Theory, provide a language or set of terms for articulating the attributes of the film?

Take advantage of class resources to explore possible approaches to cultural/historical film analyses, and find out whether you will be expected to do additional research into the film’s context.

Mise-en-scène analysis

A mise-en-scène analysis attends to how the filmmakers have arranged compositional elements in a film and specifically within a scene or even a single shot. This type of analysis organizes the individual elements of a scene to explore how they come together to produce meaning. You may focus on anything that adds meaning to the formal effect produced by a given scene, including: blocking, lighting, design, color, costume, as well as how these attributes work in conjunction with decisions related to sound, cinematography, and editing. For example, in the clip from Vertigo , a mise-en-scène analysis might ask how numerous elements, from lighting to camera angles, work together to present the viewer with the perspective of Jimmy Stewart’s character.

To conduct this type of analysis, you could ask:

• What effects are created in a scene, and what is their purpose?
• How does this scene represent the theme of the movie?
• How does a scene work to express a broader point to the film’s plot?

This detailed approach to analyzing the formal elements of film can help you come up with concrete evidence for more general film analysis assignments.

Reviewing your draft

Once you have a draft, it’s helpful to get feedback on what you’ve written to see if your analysis holds together and you’ve conveyed your point. You may not necessarily need to find someone who has seen the film! Ask a writing coach, roommate, or family member to read over your draft and share key takeaways from what you have written so far.

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

Aumont, Jacques, and Michel Marie. 1988. L’analyse Des Films . Paris: Nathan.

Media & Design Center. n.d. “Film and Cinema Research.” UNC University Libraries. Last updated February 10, 2021. https://guides.lib.unc.edu/filmresearch .

Oxford Royale Academy. n.d. “7 Ways to Watch Film.” Oxford Royale Academy. Accessed April 2021. https://www.oxford-royale.com/articles/7-ways-watch-films-critically/ .

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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Each week either Bryon, Shaun, or Ron will pick a movie and with Movie's Shouting grading system they will determine the grade of that movie.   After years of research and countless hours fine tuning, Movie Shouting has determined the greatest grading system to evaluate movies on.  Every movie starts at 100 and if you like something you add points and if you don’t like something you subtract points.

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• NOV 22, 2023

The Karate Kid

Movie Shouting’s 8th podcast. Please join Bryon, Shaun, and Ron as they discuss one of Bryon's picks "Karate Kid".  After years of research and countless hours fine tuning, Movie Shouting has determined the greatest grading system to evaluate movies on.  Every movie starts at 100 and if you like something you add points and if you don’t like something you subtract points. Daniel, played by Ralph Macchio moves to California with his mother, Lucille, played by Randee Heller, but quickly finds himself the target of a group of bullies.  These bullies study karate at the Cobra Kai dojo. Daniel befriends Mr. Miyagi, played by Noriyuki "Pat" Morita, a repairman who just happens to be a martial arts master. Miyagi takes Daniel under his wing, training him in a more compassionate form of karate and preparing him to compete against Cobra Kai. Support the show

• NOV 15, 2023

Movie Shouting’s 7th podcast. Please join Bryon, Shaun, and Ron as they discuss one of Ron's picks "The Godfather".  After years of research and countless hours fine tuning, Movie Shouting has determined the greatest grading system to evaluate movies on.  Every movie starts at 100 and if you like something you add points and if you don’t like something you subtract points. Akira is a 1988 Japanese animated cyberpunk film, directed by Katsuhiro Otomo. Based on Otomo's 1982 manga. Set in a dystopian 2019, it tells the story of Shōtarō Kaneda, the leader of a biker gang whose friend, Tetsuo Shima, acquires incredible abilities after an accident, eventually threatening an entire military complex amid chaos and rebellion in the sprawling futuristic city of Neo-Tokyo. Support the show

• 1 hr 22 min
• NOV 8, 2023

The Godfather

Movie Shouting’s 6th podcast. Please join Bryon, Shaun, and Ron as they discuss one of Ron's picks "The Godfather".  After years of research and countless hours fine tuning, Movie Shouting has determined the greatest grading system to evaluate movies on.  Every movie starts at 100 and if you like something you add points and if you don’t like something you subtract points. The Godfather is a 1972 epic crime film directed by Francis Ford Coppola.  Based on Mario Puzo's best-selling 1969 novel of the same name.  It stars Marlon Brando, Al Pacino, James Can, Robert Duvall and Diane Keaton.  It is the First installment in the Godfather trilogy, following the Corleone family under Vito Corleone as the crime family transforms to his youngest son, Michael Corleone.  Michael goes from reluctant family outsider to ruthless mafia boss. Support the show

• 1 hr 19 min
• OCT 29, 2023

Repo! The Generic Opera

Movie Shouting’s 5th podcast. Please join Bryon, Shaun, and Ron as they discuss one of Bryon's picks "Repo! The Genetic Opera".  After years of research and countless hours fine tuning, Movie Shouting has determined the greatest grading system to evaluate movies on.  Every movie starts at 100 and if you like something you add points and if you don’t like something you subtract points. Repo! The Genetic Opera is a 2008 American goth rock opera.  Directed by Darren Lynn Bousman, written and composed by Darren Smith and Terrance Zdunic.   The film stars Alexa Vega, Paul Sorvino, Anthony Stewart Head, Sarah Brightman, Paris Hilton, Bill Moseley, Nivek Ogre and Zdunich. In the not-too-distant future, an epidemic of organ failures leads to the rise of Gene Co.  Those who miss their payments become targets of mercenaries, who repossess the organs. In a world of drug addiction and legalized murder, a sheltered youth (Alexa Vega) seeks a cure for her rare disease as well as information about her family's mysterious history. Support the show

• 1 hr 17 min
• OCT 5, 2023

The Greasy Strangler

Movie Shouting’s 4th podcast. Please join Bryon, Shaun, and Ron as they discuss one of Shaun's picks "The Greasy Strangler".  After years of research and countless hours fine tuning, Movie Shouting has determined the greatest grading system to evaluate movies on.  Every movie starts at 100 and if you like something you add points and if you don’t like something you subtract points. The Greasy Strangler is a 2016 black comedy horror film directed by Jim Hosking, and written by Hosking and Toby Harvard. The film stars Michael St. Michaels, Sky Elobar, Elizabeth De Razzo, Gil Gex, Abdoulaye NGom and Holland MacFallister.  Ronnie (Michael St. Michaels) runs a disco walking tour along with his son, Brayden (Sky Elobar). When a sexy, alluring woman named Janet (Elizabeth De Razzo) takes the tour, it begins a competition between father and son for her attentions. It also brings about the appearance of an oily, inhuman maniac who stalks the streets at night and strangles the innocent dubbed the Greasy Strangler. Support the show

• SEP 26, 2023

Citizen Kane

Movie Shouting’s 3rd podcast. Please join Bryon, Shaun, and Ron as they discuss one of Ron’s favorite movies “Citizen Kane”.  After years of research and countless hours fine tuning, Movie Shouting has determined the greatest grading system to evaluate movies on.  Every movie starts at 100 and if you like something you add points and if you don’t like something you subtract points.  Citizen Kane is a 1941 drama film directed, produced, and starring Orson Welles.  This film was Orson Welles first feature and is often cited as the greatest film ever made.  It topped the American Film Institutes “100 Years…100 Movies” list in 1998 and in 2007 when the list was updated.    In this film, a reporter is assigned to decipher Charles Foster Kane’s (Orson Welles) dying words “Rose Bud”.  His investigation takes him on a journey across Kane’s life.  He hears tales from friend Jedediah Leland (Joseph Cotton) to his mistress then turned wife Susan Alexander (Dorothy Comingore) and many, many more.  Each one giving a fragment of the Kane and his mysterious final word.   Support the show

• 1 hr 27 min
• © 2024 MOVIE SHOUTING

Top Podcasts In TV & Film

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Movies About Scientists, Engineers, Doctors, Researchers, Inventors, Explorers and People who do Amazing Things

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1. The Imitation Game (2014)

PG-13 | 114 min | Biography, Drama, Thriller

During World War II, the English mathematical genius Alan Turing tries to crack the German Enigma code with help from fellow mathematicians while attempting to come to terms with his troubled private life.

Director: Morten Tyldum | Stars: Benedict Cumberbatch , Keira Knightley , Matthew Goode , Allen Leech

Votes: 825,246 | Gross: $91.13M

2. The Story of Louis Pasteur (1936)

Passed | 86 min | Biography, Drama, History

The biography of the pioneering French microbiologist who helped revolutionize agriculture and medicine.

Director: William Dieterle | Stars: Paul Muni , Josephine Hutchinson , Anita Louise , Donald Woods

Votes: 3,154

Very old but it's age doesn't stop it from being a good movie.

3. Queen of the Desert (2015)

PG-13 | 128 min | Adventure, Biography, Drama

A chronicle of Gertrude Bell's life, a traveler, writer, archaeologist, explorer, cartographer, and political attaché for the British Empire at the dawn of the twentieth century.

Director: Werner Herzog | Stars: Nicole Kidman , James Franco , Robert Pattinson , Damian Lewis

Votes: 12,582

4. Einstein and Eddington (2008 TV Movie)

TV-PG | 94 min | Biography, Drama, History

Drama about the development of Albert Einstein's theory of general relativity, and Einstein's relationship with British scientist Sir Arthur Eddington, the first physicist to experimentally prove his ideas.

Director: Philip Martin | Stars: David Tennant , Richard McCabe , Patrick Kennedy , Benjamin Uttley

Votes: 7,638

5. Einstein's Big Idea (2005 TV Movie)

120 min | Documentary, Biography, Drama

This docudrama examines the history of scientific discovery that lead up to Albert Einstein's famous equation E=mc2 and its aftermath in the creation of nuclear energy. This includes ... See full summary  »

Director: Gary Johnstone | Stars: Aidan McArdle , Shirley Henderson , Steven Robertson , Gregory Fox-Murphy

6. Shackleton (2002)

103 min | Adventure, Biography, Drama

The true story of Shackleton's 1914 Endurance expedition to the South Pole, and his epic struggle to lead his twenty-eight man crew to safety after his ship was crushed in the pack ice.

Stars: Kenneth Branagh , Eve Best , Mark Tandy , Embeth Davidtz

Votes: 3,092

7. Longitude (2000)

200 min | Drama, History

In two parallel stories, the clockmaker John Harrison builds the marine chronometer for safe navigation at sea in the 18th Century and the horologist Rupert Gould becomes obsessed with restoring it in the 20th Century.

Stars: Jeremy Irons , Anna Chancellor , Emma Kay , Samuel West

Votes: 2,431

8. Doomsday Gun (1994 TV Movie)

Unrated | 120 min | Action, Drama, History

How the brilliant Canadian munitions engineer, Dr. Gerald Bull, agreed to build a super-gun for Saddam Hussein in 1988, when the U.S. cut his funding for the experiment, and how it attracted the attention of several intelligence agencies.

Director: Robert Young | Stars: Frank Langella , Alan Arkin , Kevin Spacey , Michael Kitchen

Votes: 1,196

9. Captain Cook: Obsession and Discovery (2007– )

54 min | Documentary, History

This documentary tells the real story of the life and times of Captain James Cook; the greatest explorer in history who traveled to Australia and New Zealand. His three voyages pushed the ... See full summary  »

Stars: Matt Young , Bridget Bezanson , Vanessa Collingridge , Huw Lewis-Jones

10. Captain James Cook (1987–2000)

480 min | Biography, Drama, History

During Captain James Cook's first voyage, in 1770, he discovered the east coast of Australia. He later recommended Australia as a future British colony.

Stars: Keith Michell , John Gregg , Erich Hallhuber , Jacques Penot

11. Creation (I) (2009)

PG-13 | 108 min | Biography, Drama, Romance

Torn between faith and science, and suffering hallucinations, English naturalist Charles Darwin struggles to complete 'On the Origin of Species' and maintain his relationship with his wife.

Director: Jon Amiel | Stars: Paul Bettany , Jennifer Connelly , Ian Kelly , Guy Henry

Votes: 15,133 | Gross: $0.34M

12. Seven Wonders of the Industrial World (2003)

TV-PG | 50 min | Documentary, Drama, History

Drama-documentary series which describes some of the key technological achievements of the industrial age.

Stars: Robert Lindsay , Ron Cook , John Walters , Michael Carter

13. Gorillas in the Mist (1988)

PG-13 | 129 min | Biography, Drama

The story of Dian Fossey , a scientist who came to Africa to study the vanishing mountain gorillas, and later fought to protect them.

Director: Michael Apted | Stars: Sigourney Weaver , Bryan Brown , Julie Harris , John Omirah Miluwi

Votes: 29,201 | Gross: $24.72M

14. Breaking the Mould (2009 TV Movie)

80 min | Drama, History

A historical drama that tells the story of the development of penicillin in the 1930's/40's, by a group of scientists in Oxford at The Dunn School of Pathology

Director: Peter Hoar | Stars: Dominic West , Denis Lawson , Oliver Dimsdale , Joe Armstrong

15. Ingenious (2009)

R | 85 min | Comedy, Drama, Romance

A rags-to-riches story of two friends, a small-time inventor and a sharky salesman, who hit rock bottom before coming up with a gizmo that becomes a worldwide phenomenon.

Director: Jeff Balsmeyer | Stars: Dallas Roberts , Jeremy Renner , Ayelet Zurer , Marguerite Moreau

Votes: 2,123

16. Marco Polo (2007 TV Movie)

Not Rated | 165 min | Adventure, Drama

The series follows the adventures and discoveries of the Venetian, Marco Polo.

Director: Kevin Connor | Stars: Ian Somerhalder , BD Wong , Desiree Siahaan , Brian Dennehy

Votes: 1,554

17. The Aviator (2004)

PG-13 | 170 min | Biography, Drama

A biopic depicting the early years of legendary director and aviator Howard Hughes ' career from the late 1920s to the mid 1940s.

Director: Martin Scorsese | Stars: Leonardo DiCaprio , Cate Blanchett , Kate Beckinsale , John C. Reilly

Votes: 384,151 | Gross: $102.61M

18. The Social Network (2010)

PG-13 | 120 min | Biography, Drama

As Harvard student Mark Zuckerberg creates the social networking site that would become known as Facebook, he is sued by the twins who claimed he stole their idea and by the co-founder who was later squeezed out of the business.

Director: David Fincher | Stars: Jesse Eisenberg , Andrew Garfield , Justin Timberlake , Rooney Mara

Votes: 758,938 | Gross: $96.96M

19. Apollo 13 (I) (1995)

PG | 140 min | Adventure, Drama, History

NASA must devise a strategy to return Apollo 13 to Earth safely after the spacecraft undergoes massive internal damage putting the lives of the three astronauts on board in jeopardy.

Director: Ron Howard | Stars: Tom Hanks , Bill Paxton , Kevin Bacon , Gary Sinise

Votes: 315,673 | Gross: $173.84M

20. Fat Man and Little Boy (1989)

PG-13 | 127 min | Biography, Drama, History

This film reenacts the Manhattan Project, the secret WWII project, and the first atomic bombs designed, built, and tested in Los Alamos.

Director: Roland Joffé | Stars: Paul Newman , Dwight Schultz , Bonnie Bedelia , John Cusack

Votes: 9,130 | Gross: $3.56M

21. No Highway in the Sky (1951)

Not Rated | 98 min | Drama, Thriller

An aeronautical engineer predicts that a new model of plane will fail catastrophically and in a novel manner after a specific number flying hours.

Director: Henry Koster | Stars: James Stewart , Marlene Dietrich , Glynis Johns , Jack Hawkins

Votes: 4,459

22. Dr. Ehrlich's Magic Bullet (1940)

Approved | 103 min | Biography, Drama

True story of the doctor who considered it was not immoral to search for a drug that would cure syphillis.

Director: William Dieterle | Stars: Edward G. Robinson , Ruth Gordon , Otto Kruger , Donald Crisp

Votes: 1,827

23. Something the Lord Made (2004 TV Movie)

TV-PG | 110 min | Biography, Drama

A dramatization of the relationship between heart surgery pioneers Alfred Blalock and Vivien Thomas.

Director: Joseph Sargent | Stars: Cliff McMullen , Yasiin Bey , Luray Cooper , Alan Rickman

Votes: 15,367

24. I Aim at the Stars (1960)

Approved | 107 min | Biography, Drama

The story of rocket scientist Dr. Werner von Braun's career, from the 1920s until the late 1950s.

Director: J. Lee Thompson | Stars: Curd Jürgens , Victoria Shaw , Herbert Lom , Gia Scala

25. Awakenings (1990)

PG-13 | 121 min | Biography, Drama

The victims of an encephalitis epidemic many years ago have been catatonic ever since, but now a new drug offers the prospect of reviving them.

Director: Penny Marshall | Stars: Robert De Niro , Robin Williams , Julie Kavner , Ruth Nelson

Votes: 158,805 | Gross: $52.10M

26. Touching the Void (2003)

R | 106 min | Documentary, Adventure, Drama

The true story of two climbers and their perilous journey up the west face of Siula Grande in the Peruvian Andes in 1985.

Director: Kevin Macdonald | Stars: Simon Yates , Joe Simpson , Brendan Mackey , Nicholas Aaron

Votes: 38,110 | Gross: $4.59M

27. Temple Grandin (2010 TV Movie)

TV-PG | 107 min | Biography, Drama

A biopic of Temple Grandin , an autistic woman who has become one of the top scientists in the humane livestock handling industry.

Director: Mick Jackson | Stars: Claire Danes , Julia Ormond , David Strathairn , Catherine O'Hara

Votes: 32,583

28. Jobs (2013)

PG-13 | 128 min | Biography, Drama

The story of Steve Jobs ' ascension from college dropout into one of the most revered creative entrepreneurs of the 20th century.

Director: Joshua Michael Stern | Stars: Ashton Kutcher , Dermot Mulroney , Josh Gad , Lukas Haas

Votes: 103,884 | Gross: $16.13M

29. Reach for the Sky (1956)

Approved | 123 min | Biography, Drama, War

Biopic of RAF Group Captain Douglas Bader who, after having lost both legs, flew a British fighter plane during WWII.

Director: Lewis Gilbert | Stars: Kenneth More , Muriel Pavlow , Lyndon Brook , Lee Patterson

Votes: 2,993

30. Mary Bryant (2005–2007)

R | 62 min | Adventure, Drama

A young woman is transported to the New South Wales penal colony in 1788.

Stars: Romola Garai , Jack Davenport , Alex O'Loughlin , Sam Neill

Votes: 2,578

31. Hidden Figures (2016)

PG | 127 min | Biography, Drama, History

The story of a team of female African-American mathematicians who served a vital role in NASA during the early years of the U.S. space program.

Director: Theodore Melfi | Stars: Taraji P. Henson , Octavia Spencer , Janelle Monáe , Kevin Costner

Votes: 255,349 | Gross: $169.61M

32. Kon-Tiki (2012)

PG-13 | 118 min | Adventure, Biography, Drama

Legendary explorer Thor Heyerdahl's epic 4,300-mile crossing of the Pacific on a balsawood raft in 1947, in an effort to prove that it was possible for South Americans to settle in Polynesia in pre-Columbian times.

Directors: Joachim Rønning , Espen Sandberg | Stars: Pål Sverre Hagen , Anders Baasmo , Gustaf Skarsgård , Odd-Magnus Williamson

Votes: 51,977 | Gross: $1.52M

33. The Life of Leonardo Da Vinci (1971)

300 min | Biography, History

Miniseries dramatizing the life of the Italian Renaissance genius Leonardo da Vinci (1452-1519).

Stars: Philippe Leroy , Giulio Bosetti , Giorgio Piazza , Bruno Piergentili

34. The Way Back (I) (2010)

PG-13 | 133 min | Adventure, Drama, History

Siberian gulag escapees travel four thousand miles by foot to freedom in India.

Director: Peter Weir | Stars: Jim Sturgess , Ed Harris , Colin Farrell , Dragos Bucur

Votes: 122,024 | Gross: $2.70M

A true story about a long journey to freedom.

35. The Theory of Everything (2014)

PG-13 | 123 min | Biography, Drama, Romance

Stephen Hawking gets unprecedented success in the field of physics despite being diagnosed with motor neuron disease at the age of 21. He defeats awful odds as his first wife Jane aids him loyally.

Director: James Marsh | Stars: Eddie Redmayne , Felicity Jones , Tom Prior , Sophie Perry

Votes: 482,189 | Gross: $35.89M

36. A Brilliant Young Mind (2014)

PG-13 | 111 min | Drama, Romance

A socially awkward teenage maths prodigy finds new confidence and new friendships when he lands a spot on the British squad at the International Mathematics Olympiad.

Director: Morgan Matthews | Stars: Asa Butterfield , Rafe Spall , Sally Hawkins , Eddie Marsan

Votes: 30,319 | Gross: $0.14M

37. The Man Who Knew Infinity (2015)

PG-13 | 108 min | Biography, Drama

The story of the life and academic career of the pioneer Indian mathematician, Srinivasa Ramanujan , and his friendship with his mentor, Professor G.H. Hardy.

Director: Matt Brown | Stars: Dev Patel , Jeremy Irons , Malcolm Sinclair , Raghuvir Joshi

Votes: 62,157 | Gross: $3.87M

38. Beatrix: The Early Life of Beatrix Potter (1990 TV Movie)

Biography, Drama

Local News | Movies renew interest in ‘Dune’ author…

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Local News | Movies renew interest in ‘Dune’ author archives at Pollak Library

Frank herbert's notes, letters a popular resource.

Interest in Herbert’s typewritten drafts, letters and forward-thinking ideas about the environment is surging once again.

In 1957, Herbert studied the shifting sands of the Oregon coast, a subject that fascinated him — they had been known “to swallow whole cities, lakes, rivers, highways,” he wrote in a letter to his literary agent, pitching it as a magazine article. No editor wanted it, but those dunes later took shape as the landscapes of the planet Arrakis in “Dune.”

The Pollak Library collection includes notes, manuscripts and 20 rejection letters, criticizing the book’s length and complicated plot. (Photo by Nicole Gregory, contributing photographer, courtesy of Willis E. McNelly Science Fiction Collection, Frank Herbert Papers (Collection) SC-06-FH, CSUF University Archives and Special Collections)

The story of “Dune” first appeared in the December 1963 edition of “Analog,” a science fiction journal, where it was printed in serial form. Copies of “Analog” can be found in the archival boxes at the library as well as correspondence Herbert had with his agent and writer friends, reviews of his books, notes, and heartfelt fan letters.

Like most writers, Herbert had his share of rejection. The archival boxes of his papers in Pollak Library contain rejection letters Herbert received for “Dune” from publishers unwilling to bring it out as a book — there were 20 in total — with complaints that the novel was too long and the plot too complicated.

To date, the book has sold approximately 20 million copies worldwide.

Teachers integrate Herbert’s archives in classroom studies, from literature to religion.

The rough manuscript pages for “Dune” — a story many students know only from the movies —reveal the workings of a writer’s imagination. They are filled with scribbled notes and edits.

“It’s very cool,” said Lisa A. Mix, director of the Archives and Special Collections. “You can really get a sense of his process. You can see where he crossed things out and wrote things in. I tell students, ‘This is the primitive version of track change,’ ” she said.

Herbert’s papers also offer a glimpse into the beginnings of the environmental movement in America. “Students are interested in the ‘Dune’ manuscript, but also Herbert’s research materials and notes in relation to the environment and climate change, and how those issues are portrayed in science fiction,” Mix said.

“A professor in anthropology had his students work on an exhibit on the anthropology of religion. Some of the parts of the Herbert papers and ‘Dune’ were a part of that because Herbert invents several religions in ‘Dune,’ ” she said.

The Frank Herbert archives are part of a larger collection called the Willis McNelly Science Fiction Collection, Mix said.

“Willis McNelly was a faculty member here at Cal State Fullerton in the Department of English,” she said. “In the 1960s, he was instrumental in getting science fiction recognized as literature. He was very active in both the Science Fiction Research Association and the Science Fiction Writers of America.”

In 1967, McNelly was a speaker at the Science Fiction Writers of America meeting in Berkeley.

“At that meeting, he met several science fiction writers, including Frank Herbert,” Mix said. “Because he was teaching science fiction as literature, he developed friendships and professional relationships with Frank Herbert, Ray Bradbury, Philip K. Dick and other science fiction writers. He was very aware of the importance of these writers, preserving their manuscripts, as he put it, ‘not just keeping them in boxes in their garage,’ but having them preserved eventually in a library where scholars could come and do research on them.”

Herbert donated original manuscripts and other papers to Cal State Fullerton in his lifetime, and after he died in 1986, his widow donated more. In 2015, Cal State University had a yearlong celebration of the 50th anniversary of the publication of “Dune” in which some of the archival material was included in displays.

Herbert was born in Tacoma, Washington in 1920 and knew early on that he wanted to be a writer. He worked for several newspapers before eventually becoming a freelance writer.

The Pollak Library Special Collections also includes the Freedom Center, which consists of political literature and ephemera “from all aspects of all points on the political spectrum — from the Daughters of the Confederacy to the Bernie Sanders campaign,” Mix said.

There is also a large local history collection, much of it about the growth of the citrus industry in Orange County, as well as the university archives and an extensive map collection, which includes several showing California as an island.

Relics in these collections tell stories of historic moments but also of how people were thinking at the time and shaped their history.

“One thing I like about archives is that you can look at an issue from many sides, and you can find different documentation from different perspectives,” Mix said. “I find that very interesting.”

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What Is the 'Ideal' Movie Running Time? Poll Claims Americans Prefer 92 Minutes

Is tolerance for longer movies declining?

The perfect movie length is 92 minutes — at least, according to a new study.

Market research company Talker Research released the results of their poll on Wednesday, April 24. The study was conducted online from April 3 to April 8, with 2,000 American adults participating. 

Of those, only two percent surveyed “thought a movie should be longer than two and a half hours,” per the study. Instead, 92 minutes — just over an hour and a half — was determined “as the ideal average length.”

Furthermore, only 15 percent of those polled “want to sit through a movie that’s two hours or longer.” And 23 percent said that in the previous 60 days, they had “reluctantly sat through three or more” movies “that they felt were too long.” 

In a similar study from data and business intelligence platform Statista in 2022 , also polling around 2,000 Americans over age 18, 48 percent claimed they prefer two-hour movies, whereas 28 percent preferred hour-and-a-half movies. 

The new data seems to contradict box office history, especially considering three of the four highest-grossing films of all time — 1997’s Titanic , 2019’s   Avengers: Endgame and 2022’s Avatar: The Way of Water — have a run time of over three hours. The 2019 remake of The Lion King , at just under two hours, is the only movie on the list of highest earners that comes close to 92 minutes. 

Looking at last year’s box office champions, top-grosser Barbie similarly came in at just under two hours. Best Picture Oscar winner Oppenheimer , 2023’s third-highest champ and a record-breaker for the biggest biopic of all time, clocks in at exactly three hours. 

Interestingly, last year’s second-biggest success, The Super Mario Bros. Movie , hit Talker Research’s “ideal” length at exactly 92 minutes. So do such box office smashes as 1998’s Beetlejuice , 2004’s Dodgeball and 2008’s Kung Fu Panda .

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Despite audiences’ apparent distaste for long movies, a study published last year by the website What to Watch proved that hit films are getting longer. It looked strictly at the box office success of recent decades, determining that the average movie length has increased from 110 minutes in 1981 to 141 minutes in 2022. The biggest hit of 2024 so far is Dune: Part Two , which almost reaches the three-hour mark. 

Chiabella James / Warner Bros. Pictures / The Hollywood Archive / Alamy

Of course, moviegoing has changed significantly in that time, particularly thanks to the rise of streaming content and the COVID-19 pandemic impacting turnout at cinemas. Streaming data remains difficult to track down and it’s unclear if new studies reflect big-screen movie experiences, at-home viewing or both. 

Talker Research’s poll further asked participants about subtitles in film. Whether to watch with or without subtitles at home is a “total non-issue” for 77 percent, while 15 percent said it’s a “hotly debated topic in their household.” Younger generations, meanwhile, are more likely to “always” watch movies with subtitles. 

New study answers what exactly is the perfect movie length

Americans are officially over marathon movies.

According to new research, the perfect movie length is just 92 minutes.

The poll of 2,000 Americans, conducted by Talker Research, found that the average person wants to spend far less time at the movie theater going forward.

While 92 minutes came out as the ideal average length, only two percent of the 2,000 U.S. adults polled thought a movie should be longer than two and a half hours.

And just 15% want to sit through a movie that’s two hours or longer.

In the past 60 days, the average respondent feels they’ve watched two movies that they felt were too long, with 23% having reluctantly sat through three or more.

The poll also aimed to uncover how the average person feels about subtitles.

According to the results, 15% say that whether subtitles should be on or off is a hotly debated topic in their household.

Though 77% say it’s a total non-issue.

A third of those polled say they “never” use subtitles when they watch TV at home, while just 16% say they “always” do.

Interestingly, this figure was vastly different among different age groups, as younger Americans were found to be huge fans of the concept.

Thirty percent of Gen Z respondents “always” watch with subtitles, with 23% of millennials saying the same.

Meanwhile, just 13% of Gen X and 12% of boomers agreed.

5 movies that are the ‘perfect’ length

• Kung Fu Panda
• Beetlejuice 
• Monsters Inc 
• Toy Story 2

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What’s the perfect movie length? Only a lightweight needs toilet or food breaks

US research suggests that 92 minutes is the optimum length for a film. But I have sat through long films that felt short and short films that felt buttock-annihilatingly long

I can still remember sitting down to Theo Angelopoulos’s legendary epic film The Travelling Players and noting that it was 222 minutes long and thinking … sure, cool, two hours and twenty-two minutes, tiny bit on the long side, OK, nothing I can’t handle. The truth hit me just as the house lights were starting to dim and that spasm of unease came back into my mind reading about the new US research survey that suggests that 92 minutes is the “perfect” length for a film .

The “perfect” length? What does that even mean? Larry David fans will remember his magnificent resentment in Curb Your Enthusiasm when someone tries to think of something nice to say about his hugely unsuccessful feature film Sour Grapes and finally says: “It was such a perfect length.” Larry replies acidly: “What about the width? There’s some great width in that movie!” Ninety-two minutes? Does that extra two minutes mean you’re not such a wimp that you can’t stand a film that goes above an hour and a half?

I can only say I have taken on films of buttock-annihilating, bladder-stress-testing massiveness. Bela Tarr’s mysterious black-and-white Hungarian meisterwerk Sátántangó weighs in at 439 minutes and if you’re already trying to divide that by 60 in your head and work out how many hours it is, then forget it, you’re too much of a lightweight. And only a lightweight wants loo breaks or food breaks. The original uncut version of Erich Von Stroheim’s silent 1924 masterpiece Greed went on “all day” at its single screening for awestruck critics and aghast executives, with the master himself reportedly sitting at the back scowling at anyone who dared ducking out to visit the restroom.

That said, an hour and a half isn’t a bad proportion. My late predecessor Derek Malcolm told me that 10% can be cut out of any film, no matter how long it is, and then 10% of that, and again, so that a film – like Zeno’s arrow – approaches a sublime existential state of brevity. In truth, there’s something to be said for the 92-minute idea. Charles Laughton’s The Night of the Hunter is 92 minutes. So is Ingmar Bergman’s Autumn Sonata, Howard Hawks’s His Girl Friday, Tim Burton’s Beetlejuice, Anthony Mann’s Winchester ’73, Pete Docter’s Monsters, Inc, and Kevin Smith’s Clerks.

Storytelling discipline and clarity in feature film-making used to mean that the hour-and-half-to-two hour benchmark got a three-act narrative across with efficiency and force and theatre managers loved it because they could schedule many separate performances a day. Now, perhaps, the primacy of streamers and binge-watching means in theory that people are ready for longer films (though perhaps not in the theatre). I have watched epic films that have zapped by very quickly, and I have watched films that were short in theory (usually commercial Hollywood pictures) but whose every minute seemed to last as long as Wagner’s Ring Cycle. Let’s not tie ourselves to a 92-minute rule.

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Film Studies Research Guide: Themes, Subjects & Characters

• Film Reviews
• Films & Videos
• Screenplays/Filmscripts
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Finding Films on Particular Topics, Themes or Characters

These three indexes of topics in films are very useful, but they are of course limited by their publication dates and the number of films they cover.

• American Film Scripts Online AFSO identifies screenplays' subject(s) and genre, and lets you search by these criteria. Note that AFSO only includes American films.
• Encyclopedia of film themes, settings and series by Richard B. Armstrong, Mary Willems Armstrong Publication Date: c2001 This encyclopedia is especially useful for obtaining examples of films with themes like amnesia, deafness, genies, ice skating, mirrors, photographers, sewers, and windmills.
• The Ultimate Movie Thesaurus by Christopher Case Publication Date: c1996 Consists of two major sections. The first is a list of films by title, providing a one-sentence synopsis, its genre(s), and its topics or themes. The second section lists the genres and topics, and the films that provide examples.

Using Orbis to Find Films on Particular Topics, Themes or Characters

Films on a specific character, character type or group are typically in the form [Character]--Drama or [Character] films . The "--Drama" subheading is the proper one, but the reason there may be two forms is that often these are considered a genre (although sometimes the subject heading is applied by mistake).

In many cases you will need to search both forms. "Drama" covers fiction movies, plays and TV shows, including fictionalized accounts of real people or events (to some extent you can limit to films by using the strategies described on the Films & Videos page). These headings may be used for both fiction and non-fiction (documentary) films. For instance, there may be a non-fiction film about the rise of the vampire movie genre.

Films on a theme or event usually have a subject heading taking the form [Topic]--Drama , but there are also related genres (e.g., War films).

Films that are part of a series (or implied series) may receive [Series name] films . A series is usually treated as though it's a genre.

NOTE: Catalogers are not required to apply any headings to films on a particular topic . You may need to supplement your searches by looking at filmographies and similar sources.

Works About a Topic, Theme or Character as Represented in Films

Critical works, including documentaries, about a topic as seen in motion pictures. (Sometimes these headings are also applied to fiction films. This is usually an error, but it's appropriate for a film like Hollywood Shuffle , which is in fact about African-American actors in Hollywood.)

Note that nearly all of these have the form [Topic] in motion pictures , but that's not true for war movies and topics treated as genres. See also Genres, Styles, Categories and Series . The phrase "in motion pictures" often covers films that aren't specifically about some topic etc., but happens to include it.

The lists below are far from exhaustive, but they should indicate the many possibilities.

Selected Reference Resources

Be sure to look at the Bibliographies & Filmographies page for more resources on how particular social groups are portrayed!

• The American Indian in film by Michael Hilger Publication Date: 1986
• The bent lens: a world guide to gay & lesbian film by Claire Jackson, Peter Tapp Publication Date: 1997
• Blacks in black and white: a source book on Black films by Henry T. Sampson Publication Date: c1995
• The Columbia companion to American history on film: how the movies have portrayed the American past by Peter C. Rollins Publication Date: 2003
• The encyclopedia of ethnic groups in Hollywood by James Robert Parish Publication Date: 2003
• Encyclopedic dictionary of women in early American films, 1895-1930 by Denise Lowe Publication Date: 2005

Director, Yale Film Archive

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• Last Updated: Mar 24, 2024 11:29 PM
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‘deadpool & wolverine’ director shawn levy says film requires no “prior research” of marvel cinematic universe.

“I am definitely not looking to do homework when I go to the movies," he says of his aim to make a movie that MCU newcomers will enjoy as much as passionate fans.

By Kimberly Nordyke

Kimberly Nordyke

Managing Editor, Digital

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With numerous films and TV series in the Marvel Cinematic Universe , it’s a daunting proposition for a newcomer to even think about getting caught up on the various characters and storylines.

But Shawn Levy , who directed the upcoming Deadpool & Wolverine , says there will be no “homework” required to enjoy his film.

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“I very much made this film with certainly a healthy respect and gratitude towards the rabid fan base that has peak fluency in the mythology and lore of these characters and this world,” he said. “But I didn’t want to presume that. This movie is built for entertainment, with no obligation to come prepared with prior research.”

The movie sees Deadpool, played by Ryan Reynolds , and Wolverine, played by Hugh Jackman , reunite on the big screen for the first time since 2009’s X-Men Origins: Wolverine. (It’s also a reunion for the actors and Levy, who previously directed Reynolds in 2021’s Free Guy and Jackman in 2011’s Real Steel .)

Since then, Jackman’s character (warning: seven-year-old spoiler alert ahead!) died in 2017’s Logan . But the folks behind Deadpool & Wolverine say that the new film won’t pose a conflict with the MCU canon or the character’s continuity.

“It’s a really interesting duo,” Levy told the AP. “They’re built for huge conflict with each other because they’re so different individually. But that makes for a very interesting story, because the best two-hander stories, whether it’s Midnight Run or 48 Hours or Planes, Trains and Automobiles, yes, it’s littered with conflict. But it’s ultimately about something more as well and that’s what audiences will see.”

Levy acknowledged the level of secrecy — and unconfirmed rumors, including speculation about a Taylor Swift cameo — but is keeping the details of the film close to the vest.

Deadpool & Wolverine hits theaters July 26.

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