On March 2nd, Matthew Jockers gave a talk at Wesleyan about his research on using quantitative methods for analysis in literature. His talk was titled “Novel Analytics: From James Joyce to the Bestseller Code.” The following article is an exploration of his talk and the ideas he brought forward.

What makes something a piece of art? This might sound like a pretty theoretical question, but English professor Matthew L. Jockers believes that it is possible to take a technical approach.

“Art shows how things are perceived, not known,” Jockers explained. This is a definition that could cause tension in the literary world. After all, writing is messy, personal, and painfully subjective. And yet – “We tend to emphasize the idea that the text is withholding an ‘essential truth,’” Jockers explained. In this way, a literary critic wants to be able to anticipate a certain meaning, causing an endless tug-of-war between objectivity and subjectivity. Jockers does not wrestle with this tension, as evidenced by his book The Bestseller Code, in which he uses analysis to tackle that all-elusive question: What makes a bestselling novel?

Jockers and his co-author Jodie Archer nailed down the qualities that make a book a bestseller by analyzing 30 years’ worth of New York Times bestselling novels. The idea of taking an analytical look at novels was appealing to Jockers because, while a literature enthusiast, he is largely interested in the way parts fit together. Rather than focusing on the novel itself, Jockers believes we should focus on the relationship readers have with the literal words on the page. “Books are a map of grammar, syntax, and word order used to direct our attention to a certain meaning,” he explained. This transforms the previous abstract question to a new, more concrete one: How do best-selling writers write?

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In light of the recent election, it is more important than ever to look at how and where we are responsible for perpetuating prejudice. In a previous article, DataCrunch introduced the concept of “Weapons of Math Destruction,” which are data models built from a limited or biased sample of data that result in toxic feedback loops. Since this explanation is most often attributed to artificial intelligence, there is little discussion about how this description could also illuminate the workings of the human mind. While many might want to think of this narrow-mindedness as below the mental capacity of human beings, such a viewpoint is dangerous in that makes having a conversation about prejudice difficult. Like artificial intelligence, humans create internal models in their minds, which often lead to the creations of ideals such a stereotypes. The flaw of these models is that they are based on the past, and if not updated or addressed they will continue to run off of the same data they were originally fed – even if times have changed.

At an individual level, racism is a toxic feedback loop. People want to be able to predict how other people will behave, and it can be far too easy to create “a binary prediction that all people of that race [or gender, sexuality, religion, political group, etc] will behave that same way,” (O’Neil, 23). If an issue like this develops in a technical model, it isn’t too difficult to go back and manually adjust the data input or change the important factors. But people with racist beliefs “don’t spend a lot of time hunting down reliable data to train their twisted models,” (23) They will continue to gladly absorb the data that seems to confirm their beliefs, and will refute data that tests them.

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In 1994, a small company called Marvel acquired the rights to sell children’s toys and comic books based off of their characters. During this time they were riding the wave of the comic book boom, a time when comic book consumption and production reached a sudden high. Marvel entered this period of success with high hopes, and followed the lead of other comic book companies to find success. This follow-the-leader approach turned against them when the market collapsed in 1997, forcing Marvel to declare bankruptcy.

All of this happened before Marvel Entertainment was the media power house we know today. Now, it seems as if Marvel is expanding into every corner of product design, churning out movie and TV series with a built in comic book and merchandise market at such a pace that some are calling this Marvel’s Golden Age. This approach is startlingly different than the company’s mantra in 1997, leading many Marvel enthusiasts to ask themselves what has changed between then and now.

Wesleyan alum Peter Olson ’97 was hired by Marvel in 2004, the year before Marvel changed their name from Marvel Enterprises to Marvel Entertainment – a move that made their expansionist dreams quite clear. Peter’s main assignment was to re-launch Marvel’s website, in a hope that they could rebuild through better online communication with fans. But Peter knew that, in order to really reach their full potential, Marvel needed to become a business. While working there, he landed on a golden question for Marvel’s future: “How can we take Marvel’s data and turn it into something useful for fans?” One of the results that came from this line of thinking was a visualization of all the character relations in the Marvel Universe, color-coded by the major franchises. Shown below, each node represents a character, and the thickness of each edge correlates to the number of interactions between the characters. Peter was only a cog in a large mechanical shift within Marvel, but the thinking that led to the creation of this data visualization is very representative of the change that took place after Marvel’s bankruptcy in 1997 – they stopped thinking about how they could use their data to merely market products, and instead focused on a way to draw in customers by using their data for interactive and proactive design.

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