When readers look at a story quantitatively, they can see patterns in the text that would otherwise remain hidden. The goal, however, is not to reduce the joy and beauty of reading to a mathematical exercise. Rather, it is to uncover new ways of finding meaning in texts–using data and visualizations to spot new places in the story to explore.
It’s easier to see what this means in action. So let’s do so, using Shakespeare’s Romeo and Juliet as an example.
Even if you haven’t read the play (recently or at all) you are probably familiar with basic story: Romeo and Juliet are two young lovers from warring families who fall for each other and in pursuing their love bring ruin on both themselves and others they care about. Like all of Shakespeare’s plays, this one occurs in five acts.
When I read the play, I was struck by the way love and death are tangled together. I’m hardly the first reader to notice such entanglement. But, look what happens if I zoom out from my own close reading of the text and instead explore the actual words “love” and “death” across the whole play.
In the chart above, you can observe how the words “love” and “death” have an often inverse relationship, with the first half of the text being more loving and the second half being more deathly. Interestingly, one can also observe that in Act 3, Shakespeare uses the two words with far more similar frequency.
Why is that? What is going on in Act 3 that might cause “love” and “death” to be used so comparably?
Rereading Act 3 reveals that it is just then that Tybalt slays Mercutio and Romeo kills Tybalt. It’s a major turning point in the play, one where the death-ridden ramifications of youthful love begin to become clear to readers. It never occurred to me before, but Tybalt’s death marks beginning of the end of Romeo and Juliet’s mutual destruction.
Whether you agree with my interpretation or not isn’t the point, of course. The point is that by simply looking at the text through a data visualization, I had new opportunities to pose questions about the story and then to explore those questions back in the text.
And you can do so with any word an author uses.
So, are you eager to try this out for yourself? If so, here is how to get started.
Getting Started
There are lots of ways to explore books computationally. Using the search databases here on Plotting Plots, here are three reliable ways to begin.
1. Plot characters’ names.
Stories usually have many significant character names. Take F. Scott Fitzgerald’s The Great Gatsby. By plotting the main characters’ name from Fitzgerald’s novel, I can see new relationships between the characters I didn’t notice before. For instance, the way Tom Buchanan and his wife Daisy are introduced with comparable frequency of words in Chapter 1, but then how exponentially Tom overshadows both Daisy and Gatsby in Chapter 7. (And if you recall the story, you will know why that is!)
2. Plot exact keywords.
Another way to look at a text quantitatively can be to plot keywords, like the Shakespearean example with “love” and “death”. But let’s look at another example to drive this home. In Christina Hammonds Reed’s The Black Kids, the author refers several times throughout the story to the Emily Dickinson poem “I am nobody.” When I read the book with a book club, I was curious how words like “nobody” and versions of the word like “know” and “body” interrelated throughout the text. So I plotted it. I was struck by the correlation between “nobody” and “body,” as well as the consistent prevalence of “know” in comparison. It led me to reread sections of the text in an effort to better understand how the main character, Ashley, grapples with her own identity in school as related to both the Blackness of her “body” and her superlative intelligence (“know”).
3. Plot related topical words.
Once you have a sense of the main topics in a text, it can be fun to look up related words. In J.K. Rowling’s Harry Potter and the Sorcerer’s Stone, I realized while reading the book with my son that Harry is described as having black hair and green eyes–which are the colors of his rival house at Hogwart’s: Slytherin. (In the movies, Harry has neither black hair nor green eyes!) This is important because Harry is unsure whether he really belongs in Slytherin’s house (the house of his parent’s killer and evil wizard Voldemort) rather than Gryffindor. A quick plot of the words “slytherin,” “gryffindor,” and “harry” shows that the names of both houses are highly correlated with each other, which might support the interpretation that Harry’s ambivalence about house membership signifies something deeper going on with his character (which becomes more evident as one reads later books in the series).
In the end, plotting plots is really less about data or visualizations and more about uncovering new ways to explore and enjoy books. As you begin to plot plots on your own, take a screenshot of the chart and share it with other readers (including me if you like, @tomliamlynch and #plottingplots). Ask others what they see, too. And perhaps together we can build a community of book lovers who are unafraid to admit that, well, they like data too.