An unexpected point of linguistic similarity between detective fiction and Shakespearean comedy recently led me to consider some of the theoretical implications of tools like DocuScope, which frequently identify textual similarities that remain invisible in the normal process of reading.
A Linguistic Approach to Suspense Plot
Playing around with a corpus of prose, we discovered that the linguistic specs associated with narrative plot are surprisingly unique. Principle Component Analysis performed on the linguistic features counted by DocuScope suggested the following relationship between the items in the corpus:
I interpreted the two strongest axes of differentiation seen in the graph (PC 1 and PC 2) as (1) narrative, and (2) plot. The two poles of the narrative axis are Wuthering Heights (most narrative) and The Communist Manifesto (least narrative). The plot axis is slightly more complicated. But on the narrative side of the spectrum, plot-driven mysteries like “The Speckled Band” and The Canterville Ghost score high on plot, while the least plotted narrative is Samuel Richardson’s Clarissa (9 vols.). For now, I won’t speculate about why Newton’s Optics scores so astronomically high on plot. It is enough that when dealing with narrative, PC 2 predicts plot.
The fact that something as qualitative and amorphous as plot has a quantitative analogue leads to several questions about the meaning of the data tools like DocuScope turn up.
Linguistic Plot without Actual Plot
Because linguistic plot is quantifiable, it allows us to look for passages where plot is present to a relative degree. Given a large enough sample, it is more than likely that some relatively plotted passages will occur in texts that are not plotted in any normal sense. This would at minimum raise questions about how to handle genre boundaries in digital literary research.
Our relative-emplotment test (done in TextViewer) yielded intuitive results when performed on the dozen or so stories in The Adventures of Sherlock Holmes: the passages exhibiting the strongest examples of linguistic plot generally narrated moments of discovery, and moved the actual plot forward in significant ways. Often, these passages showed Holmes and Watson bursting into locked rooms and finding bodies.
When we performed the same test on the Shakespeare corpus, something intriguing happened. The passages identified by TextViewer as exhibiting linguistic plot look very different from the corresponding passages in Sherlock Holmes. There were no dead bodies, no broken-down doors, and no exciting discoveries. Nonetheless, the ‘plotted’ Shakespeare scenes were remarkably consistent with each other. Perhaps most significant in the context of their genre, these scenes had a strong tendency to show characters putting on performances for other characters. Additionally, in a factor that is fascinating even though it is probably a red herring, the ‘plotted’ Shakespeare scenes had an equally strong tendency to involve fairies.
The consistent nature of the ‘plotted’ Shakespeare scenes suggests that the linguistic specs associated with plot when they occur in Sherlock Holmes may have different, but equally specific, effects in other genres. The next step would be to find a meaningful correspondence between the two seemingly disparate literary devices that accompany linguistic plot – detectives bursting into rooms to solve murders, and plays within plays involving fairies. I have some hunches about this. But in many ways the more important question is what is at stake in using DocuScope to identify such unexpected points of overlap.
Enough measurable links between seemingly unlike texts could suggest an invisible web of cognates, which share an underlying structure despite their different appearances and literary classifications. Accordingly, we might hypothesize that reading involves selective ignorance of semantic similarities that could otherwise lead to the socially deviant perception that A Midsummer Night’s Dream resembles a Sherlock Holmes mystery.
The question, then, is this: if the act of reading consists in part of ignoring unfruitful similarities, then what happens when these similarities nonetheless become apparent to us? Looking back at the corpus graph, we begin to see all sorts of possibilities, many of which would be enough make us literary outcasts if voiced in the wrong company. Could Newton’s Optics contain the most exciting suspense plot no one has ever noticed? Could Martin Luther be secretly more sentimental than Clarissa?
Estranging Capacities of Digital Cognates
I have been using the term ‘cognate’ to describe the relationship between linguistically similar but otherwise dissimilar texts. These correspondences will only be meaningful if we can connect them in a plausible way to our readerly understanding of the texts or genres in question. In the case of detective fiction and Shakespearean comedy, this remains to be seen. But our current lack of an explanation does not mean we should feel shy about pursuing the cognates computers direct us to. My analogy is the pop-culture ritual of watching The Wizard of Oz, starting the Pink Floyd album Dark Side of the Moon on the third roar of the MGM lion. The movie and the record sync up in a plausible pattern, prompting the audience to grasp a connection between the cognate genres of children’s movies and psychedelic rock.
If digital methods routinely direct our attention to patterns we would never notice in the normal process of reading, then we can expect them to turn up a large number of such cognates. If we want to understand the results these tools are turning up, we should develop a terminology and start thinking about implications – not just for the few correspondences we can explain, but also for the vast number we cannot explain, at least right now.
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