Objectives

Our project aims to show how many movies in the last 20 years (2001 – 2021) have passed the Bechdel test. The Bechdel test, or Bechdel-Wallace test, is a simple test for movies which names the following of three criteria: 1) it has to have at least two [named] women in it, who 2) talk to each other, about 3) something besides a man (Bechdel Test Movie List, n.d.). The main objective for our project is to display whether there has been a change in movie production and analyze whether the movies have become more gender equal.

Since mass media (film in this case) is an agent of socialization (Macionis et al. 2017), it is important that it represents minority groups in a way that will not negatively shape the users’ view. Studies have shown that stereotyped representation in media reflects and distorts how minority groups see themselves and how they are seen by others (Hudson 1998, Hook 1992). Although in later years there have been improvements in women’s representation on screen (Women and Hollywood, n.d.), many of the female characters can be stereotyped roles. With the help of InfoVis techniques, this project will help viewers understand the change of pattern (or a lack of change) of representation in movies and find out whether this mass media platform has been doing a part to set not as stereotyped examples to the public.

 

Our goal was to analyze how many Bechdel criterias a movie has passed (1, 2, or all 3), in connection to its production year, and genre.

 

This infographic can be used by film studies and film production students and scholars, gender studies students and scholars, and movie fans who want to learn more about the industry.

 

Data

For our project, we have used data from an open data source – the official Bechdel test website. To gather information on movie genres, we will use another open data source provided by IMDB. We were able to connect our attributes smoothly, using a name as a connecting variable. After the cleaning and connecting, our dataset consisted of 6 attributes (title, year, IMDb ID, Bechdel test rating, year, genre) and 5,660 items. Our dataset had categorical data (movie names, pass/fail indication, genres) and ordinal data (years).

 

Tools Used

To sort and clean our data before importing it into Tableau, we have used Microsoft Excel. The data we collected was not cleaned, so it contained a lot of irrelevant information that we had to filter out, such as information for the movies created earlier than 2001 and later than 2021. Since both of us were familiar with Microsoft Excel before, using the software was relatively easy.

 

To merge the datasets and to create the visualizations, we have used Tableau. Many Tableau techniques learnt in class and on our own time proved to be very helpful in creating and effective and expressive visualization.

 

Analytical Steps

We came up with the concept after talking about how even popular movies do not always have the representation of the women that women deserve. Eduarda knew the principle of the Bechdel test, so we browsed the Bechdel test website for a while and realized that it can be an interesting source for our project.

 

In recent years, there have been a lot of talks in the media industry about the more fair representation of minorities, so we decided to check if women have become more represented during the last 20 years.

 

Since at the beginning of our project we did not know whether the moviemaking traditions changed in the past 20 years, we focused on producing the knowledge, rather than presenting evidence to support the existing argument. Only after creating the visualization, we were able to analyze the patterns of womens’ representation in different movie genres, and answer our own question.

 

Our first steps were gathering and cleaning the data. We discovered that most of the data comes in txt. format, so our first challenge was to convert it into an Excel format. After this, we removed the data for all the movies created before 2001 and after 2021. Since the movies in the original data tables were not organized by year, we had to use the “filter” tool in Excel. After that, we removed the odd attributes, such as its ESRB rate, and its rating. Then we organized them by year, using “sort” toll in Excel.

 

To make the dataset more suitable to work on the first scatterplot (How many movies passed all three criteria of the Bechdel test), we have created a separate dataset with information on the movies which passed all three criteria. We have created it using a “Data-Filter” function and saved it as a separate Excel sheet.

 

Then we uploaded the datasets into Tableau and merged the tables. After that, we realized that most of our movies have more than one genre. So, we decided to group the genres based on the first one. For example, if one movie was assigned as “comedy, musical”, and another – as “comedy, action”, both of them were grouped as “comedy”.

 

Then our data was clean, organized and ready to be visualized.

 

Design Process

When thinking about the best way to visualize our data we decided to look for inspiration online. We were able to see many different possibilities and from there choose the one that thought would work the best. We sketched and specified what are the parameters and what we wanted to tell about the data. When working with the data we decided that as “genres” are categories they would be better represented with different hues as we did not want to show order. The graphs that used these colours were the scatterplot and the pie chart. The other attributes of the pie chart were the spatial region and size which allowed us to easily show what genres passed the test more frequently. For the scatterplot, we were able to show the genres with the colours and the passage of time and the amount of movies with spatial region. The scatterplot was key to conveying this categorical information in an easily readable way. Meanwhile, for the line graph we wanted to show the trend and amount for the 4 criterias of the Bechdel: not pass, pass 1, pass 2 and pass all. We used a grayscale for darkest being “not pass” and lighter being pass all criterias. This was deliberated in order to give a sense of order.

We also wanted to implement interactions in our data. So we have a filter by genre which affects the whole worksheet. For the scatterplot allows you to easily play and understand the movie numbers according to genre. It also affects and helps to see the changes in how many movies did not pass or passed 1, 2 or 3 of the criteria (line graph). We also created a filter by year, which allows to select determine periods. This affects all the visualizations in the worksheet but especially our pie chart as it sums all years instead of giving a yearly breakdown as the scatterplot and line chart.

 

The Story

Movies have a huge part in shaping one person’s mind. Mass Media (film in this case) is an agent of socialization (Macionis et al. 2017). It is important that it represents minority groups in a way that will not negatively shape the users’ views. Studies have shown that stereotyped representation in media reflects and distorts how minority groups see themselves and how they are seen by others (Hudson 1998, Hook 1992). Given such an important role in society, we would expect most movies in Hollywood to pass all 3 criteria of the Bechdel test, however, the story our InfoVis tells is different. With an overall increase in creating a gender-equal society, one would imagine that graphs showing the movies that passed the Bechdel test would have an upward slope as the years go by, but our graph shows us a different story. It tells us the peak year for movies that passed the Bechdel test and what genres of movies are performing better in that sense. It can also tell whether these genres continued performing in the same manner or whether it had downfalls or improvements. Overall, we are trying to expose with data the truth behind stereotyped roles in Hollywood stories. That despite beliefs of it getting better, evidence shows otherwise.

 

Limitations

Our visualizations are great at showing how Hollywood has done better in having more women’s presence on screen, especially when you are looking at specific genres. But this also makes you question the dataset. Have the movies really decreased or is the data not being accounted for as much as it was in 2007? (Despite the data having the same yearly amount overall one may doubt it). Another point is that the movies that pass all three tests are actually way higher than the ones that do not. This is positive when looking for fewer stereotype roles but it also raises more questions. Maybe the individuals filling up the data were unconsciously biased and were looking for movies that did pass the test. Another visualization con is that movies that do not entirely pass the Bechdel test are separated into 3 categories, which means the number of movies that do not pass the Bechdel test may be higher when combined than the ones that do pass for certain parts.

Check Our Visualization Project:

https://us-west-2b.online.tableau.com/t/info419/views/Bechdel-Test-Final-Project/Dashboard1/f54fc5f6-8d44-4f5d-9892-1d6d4dc57bd4/0e3c9fbb-e12c-4d6d-8c8f-9a6387c1cc4f

Works Cited

About (n.d.). Bechdel Test Movie List. https://bechdeltest.com/.

Hooks, B. (1992). Black looks: Race and representation. Choice Reviews Online, 30(04). https://doi.org/10.5860/choice.30-2391

Hudson, S. V. (1998). Re-creational television: The paradox of change and continuity within stereotypical iconography. Sociological Inquiry, 68(2), 242–257. https://doi.org/10.1111/j.1475-682x.1998.tb00464.x

Macionis, J. J., Jansson, M., Benoit, C., & Burkowicz, J. (2021). Society: The basics. Pearson.

Munzner, Tamara (2014). Visualization Analysis and Design. CRC Press, Taylor & Francis Group.

Statistics. Women and Hollywood. (n.d.). Retrieved from https://womenandhollywood.com/resources/statistics/2021-statistics/