Distant Reading and the “Evolution” Metaphor

1

Are there any corpora that purposefully avoid “diachronicity”? There are corpora that possess no meta-data about publication dates and whose texts are therefore organized by some other scheme—for example, the IMDB movie review corpus, which is organized according to positive/negative polarity; its texts, as far as I know, are not arranged chronologically or coded for time in any way. And there are cases where time-related data are not available, easily or at all. But have any corpora been compiled with dates—the time element—purposefully elided? Is time ever left out of a corpus because that information might be considered “noise” to researchers?

Maybe in rare situations. But for most corpora whose texts span any length of time greater than a year, the texts are, if possible, arranged chronologically or somehow tagged with date information. In this universe, time flows in one direction, so assembling hundreds or thousands of texts with meta-data related to their dates of publication means the resulting corpus will possess an inherent diachronicity whether we want it to or not. We can re-arrange the corpus for machine-learning purposes, but the “time stamp” is always there, ready to be explored. Who wouldn’t want to explore it?

If we have a lot of texts—any data, really—that span a great length of time, and if we look at features in those data across the time span, what do we end up studying? In nearly all cases, we end up studying patterns of formal change and transformation across spans of time. The “evolution” metaphor suggests itself immediately. Be honest, now, you were thinking about it the minute you compiled the corpus.

One can, of course, use “evolution” as a general synonym for change. This is probably the case for Thomas Miller’s The Evolution of College English and for many other studies whose data extend only to a limited number of representative sources. However, when it comes to distant readings, the word becomes much more tempting. The trees of Moretti’s Graphs, Maps, Trees are explicitly evolutionary:

For Darwin, ‘divergence of character’ interacts throughout history with ‘natural selection and extinction’: as variations grow apart from each other, selection intervenes, allowing only a few to survive. In a seminar a few years ago, I addressed the analogous problem of literary survival, using as a test case the early stages of British detective fiction . . . (70-71)

The same book ends with an afterword by geneticist Alberto Piazza (who worked with Luigi Luca Cavalli-Sforza on The History and Geography of Human Genes). Piazza writes:

[Moretti’s writings] struck me by their ambition to tell the ‘stories’ of literary structures, or the evolution over time and space of cultural traits considered not in their singularity, but their complexity. An evolution, in other words, ‘viewed from afar’, analogous at least in certain respects to that which I have taught and practiced in my study of genetics. (95)

Analogous at least in certain respects . . . For Moretti and Piazza, literary evolution is not just a synonym for change in literature. Biological evolution becomes a guiding metaphor (not perfect, by any means) for the processes of formal change analyzed by Moretti. Piazza continues:

The student of biological evolution is especially interested in the root of a [phylogenetic] tree (the time it originated). . . . The student of literary evolution, on the other hand, is interested not so much in the root of the tree (because it is situated in a known historical epoch) as in its trajectory, or metamorphoses. This is an interest much closer to the study of the evolution of a gene, the particular nature of whose mutations, and the filter operated by natural selection, one wants to understand . . . (112-113)

Obviously, for Piazza, Moretti’s study of changes to and migrations of literary form in time and space evokes the processes and mechanisms of biological evolution—there’s not a one-to-one correspondence, of course, and Piazza points this out at length, but the similarities are evocative enough that he, a population geneticist, felt confident publishing his thoughts on the subject.

In Distant Reading, Moretti has more recently acknowledged that the intense data collection and quantitative analysis that has marked work at Stanford’s Literary Lab must at some point heed “the need for a theoretical framework” (122). Regarding that framework, he writes:

The results of the [quantitative] exploration are finally beginning to settle, and the un-theoretical interlude is ending; in fact, a desire for a general theory of the new literary archive is slowly emerging in the world of digital humanities. It is on this new empirical terrain that the next encounter of evolutionary theory and historical materialism is likely to take place. (122)

In Macroanalysis, Matthew Jockers also acknowledges (and resists) the temptation to initiate an encounter between evolutionary theory and the quantitative, diachronic data compiled in his book:

. . . the presence of recurring themes and recurring habits of style inevitably leads us to ask the more difficult questions about influence and about whether these are links in a systematic chain or just arbitrary, coincidental anomalies in a disorganized and chaotic world of authorial creativity, intertextuality, and bidirectional dialogics . . .

“Evolution” leaps to mind as a possible explanation. Information and ideas do behave in a ways that seem evolutionary. Nevertheless, I prefer to avoid the word evolution: books are not organisms; they do not breed. The metaphor for this process breaks down quickly, and so I do better to insert myself into the safer, though perhaps more complex, tradition of literary “influence” . . . (155)

And in the last chapter to Why Literary Periods Mattered, Ted Underwood does not mention evolution at all but there is clearly an evolutionary connotation to the terms he uses to describe digital humanities’ influence on literary scholars’ conception of history:

. . . digital and quantitative methods are a valuable addition to literary study . . . because their ability to represent gradual, macroscopic change brings a healthy theoretical diversity to literary historicism . . .

. . . we need to let quantitative methods do what they do best: map broad patterns and trace gradients of change. (159, 170)

Underwood also discusses “trac[ing] processes of change” (160) and “causal continuity” (161). The entire thrust of Underwood’s argument, in fact, is that distant or quantitative readings of literature will force scholars to stop reading literary history as a series of discrete periods or sharp cultural “turns” and to view it instead as a process of gradual change in response to extra-literary forces—“Romanticism” didn’t just become “Naturalism” any more than homo erectus one decade decided to become homo sapiens.

Tracing processes of gradual, macroscopic change . . . if that doesn’t invoke evolutionary theory, I don’t know what does. Underwood doesn’t even need to use the word.

Moretti, Jockers, and Underwood are three big names in digital humanities who have recognized, either explicitly or implicitly, that distant reading puts us face to face with cultural transformation on a large, diachronic scale. Anyone working with DH methods has likely recognized the same thing. Like I said, be honest: you were already thinking about this before you learned to topic model or use the NLTK.

 

2

Human culture changes—its artifacts, its forms. This is not up for debate. Even if we think human history is a series of variations on a theme, the mutability of cultural form remains undeniable, even more undeniable than the mutability of biological form. Distant reading, done cautiously, gives us a macro-scale, quantitative view of that change, a view simply not possible to achieve at the scale of individual texts or artifacts. Given the fact of cultural transformation, then, and DH’s potential to visualize it, to quantify aspects of it, one of two positions must be taken.

1. The diachronic patterns we discover in our distant readings are, to use Jockers’ words, “just arbitrary, coincidental anomalies in a disorganized and chaotic world of authorial creativity, intertextuality, and bidirectional dialogics.” Theorizing the patterns is a fool’s errand.

2. The diachronic patterns we discover are not arbitrary or random. Theorizing the patterns is a worthwhile activity.

Either we believe that there are processes guiding cultural change (or, at least, that it’s worthwhile to discover whether or not there are such processes) or we assume a priori that no such processes exist. (A third position, I suppose, is to believe that such processes exist but we can never know them because they are too complex.) We can all decide differently. But those who adopt the first position should kindly leave the others to their work. In my view, certain criticisms of distant reading amount to an admonition that “What you’re trying to do just can’t be done.” We’ll see.

 

3

When we decide to theorize data from distant readings, what are we theorizing? Moretti, Jockers, and Underwood each provide a similar answer: we are theorizing changes to a cultural form over time and, in some instances, space. Certain questions present themselves immediately: Are the changes novel and divergent, or are they repeating and reticulating? Is the change continuous and gradual, or are there moments of punctuated equilibrium? How do we determine causation? Are purely internal mechanisms at work, or also external dynamics? A complex interplay of both internal mechanisms and external dynamics? How do we reduce data further or add layers of them to untangle the vectors of causation?

To me, all of this sounds purely evolutionary. Even talking about gradual vs. quick change is a discussion taken right out of Darwinian theory.

But we needn’t adopt the metaphor explicitly if we are troubled that it breaks down at certain points. Alex Reid writes:

Matthew Jockers remarks following his own digital-humanistic investigation, “Evolution is the word I am drawn to, and it is a word that I must ultimately eschew. Although my little corpus appears to behave in an evolutionary manner, surely it cannot be as flawlessly rule bound and elegant as evolution” (171). As he notes elsewhere, evolution is a limited metaphor for literary production because “books are not organisms; they do not breed.” He turns instead to the more familiar concept of “influence” . . . Certainly there is no reason to expect that books would “breed” in the same way biological organisms do (even though those organisms reproduce via a rich variety of means). [However], if literary production were imagined to be undertaken through a network of compositional and cognitive agents, then such productions would not be limited to the capacity of a human to be influenced. Jockers may be right that “evolution” is not the most felicitous term, primarily because of its connection to biological reproduction, but an evolutionary-type process, a process as “natural” as it is “cultural,” as “nonhuman” as it is “human,” may exist.

An “evolutionary-type” process of culture is what we’re after, one that is not necessarily reliant on human agency alone. Will it end up being “flawlessly rule bound and elegant as evolution”? First, I think Jockers seriously over-estimates the “flawless” nature of evolutionary theory and population genetics. If the theory of evolution is so flawless and elegant, and all the science settled, what do biologists and geneticists do all day? Here’s a recent statement from the NSF:

Understanding the tree of life has been a goal of evolutionary biologists since the time of Darwin. During the past decade, unprecedented gains in gathering and analyzing phylogenetic data have demonstrated increasingly complex genealogical patterns.

. . . . Our current knowledge of processes such as hybridization, endosymbiosis and lateral gene transfer makes clear that the evolutionary history of life on Earth cannot accurately be depicted as a single, typological, bifurcating tree.

Moretti, it turns out, needn’t worry so much about the fact that cultural evolution reticulates. And Jockers needn’t assume that biological evolution is elegantly settled stuff.

Secondly, as Reid argues, we needn’t hope to discover a system of influence and cultural change that can be reduced to equations. We probably won’t find any such thing. However, within all the textual data, we can optimistically hope to find regularities, patterns that can be used to make predictions about what might be found elsewhere, patterns that might connect without casuistic contrivance to theories from the sciences. Here’s an example, one I’ve used several times on this blog: Derek Mueller’s distant reading of the journal College Composition and Communication. Mueller used article citations as his object of analysis. When he counted and graphed a quarter century of citations in the journal, he discovered patterns that looked like this:

Actually, based on similar studies of academic citation patterns, we could have predicted that Mueller would discover this power law distribution. It turns out that academic citations—a purely cultural form, a textual artifact constructed through the practices of the academy—behave according to a statistical law that seems to affect all sorts of things, from earthquakes to word frequencies. This example makes a strong case against those who argue that cultural artifacts, constructed by human agents within their contextualized interactions, will not aggregate over time into scientifically recognizable patterns.  Granted, this example comes from mathematics, not evolutionary theory, but it makes the point nicely anyway: the creations of human culture are not necessarily free from non-human processes. Is it foolish to look for the effects of these processes through distant reading?

 

4

“Evolution,” “influence,” “gradualism”—whatever we call it in the digital humanities, those of us adopting it on the literary and rhetorical end have a huge advantage over those working in history: we have a well-defined, observable element, an analogue of DNA, to which we can always reduce our objects of study: words. If evolution is going to be a guiding metaphor, we need this observable element because it is through observations of its metamorphoses (in usage, frequency, etc.) that we begin to figure out the mechanisms and dynamics that actually cause or influence those metamorphoses. If we had no well-defined segment to observe and quantify, the evolutionary metaphor could be thrown right out.

To demonstrate its importance, allow me a rhetorical demonstration. First, I’ll write out Piazza’s description of biological evolution found in his afterword to Graphs, Maps, Trees. Then, I’ll reproduce the passage, substituting lexical and rhetorical terms for “genes” but leaving everything else more or less the same. Let’s see how it turns out:

Recognizing the role biological variability plays in the reconstruction of the memory of our (biological) past requires ways to visualize and elaborate data at our disposal on a geographical basis. To this end, let us consider a gene (a segment of DNA possessed of a specific, ascertainable biological function); and for each gene let us analyze its identifiable variants, or alleles. The percentage of individuals who carry a given allele may vary (very widely) from one geographical locality to another. If we can verify the presence or absence of that allele in a sufficient number of individuals living in a circumscribed and uniform geographical area, we can draw maps whose isolines will join all the points with the same proportion of alleles.

The geographical distribution of such genetic frequencies can yield indications and instruments of measurement of the greatest interest for the study of the evolutionary mechanisms that generate genetic differences between human populations. But their interpretation involves quite complex problems. When two human populations are genetically similar, the resemblance may be the result of a common historical origin, but it can also be due to their settlement in similar physical (for example, climactic) environments. Nor should we forget that styles of life and cultural attitudes of an analogous nature (for example, dietary regimes) can favour the increase or decrease to the point of extinction of certain genes.

Why do genes (and hence their frequencies) vary over time and space? They do so because the DNA sequences of which they are composed can change by accident. Such change, or mutations, occurs very rarely, and when it happens, it persists equally rarely in a given population in the long run . . . From an evolutionary point of view, the mechanism of mutation is very important because it introduces innovations . . .

. . . The evolutionary mechanism capable of chancing the genetic structure of a population most swiftly is natural selection, which favours the genetic types best adapted for survival to sexual maturity, or with a higher fertility. Natural selection, whose action is continuous over time, having to eliminate mutations that are injurious in a given habitat, is the mechanism that adapts a population to the environment that surrounds it. (100-101)

Now for the “distant reading” version:

Recognizing the role lexical variability plays in the reconstruction of the memory of our (literary and rhetorical) past requires ways to visualize and elaborate data at our disposal on the basis of cultural space (which often correlates with geography). To this end, let us consider a word (a segment of phonemes and morphemes possessed of a specific, ascertainable grammatical or semantic function); and for each word let us analyze its stylistic variants, or synonyms. The percentage of texts that carry a given stylistic variant may vary from one cultural space to another, or from one genre to the other. If we can verify the presence or absence of that variant in a sufficient number of texts produced in a circumscribed and uniform cultural space we can draw maps whose isolines will join all the points with the same proportion of stylistic variants.

The distribution of such lexical frequencies can yield indications and instruments of measurement of the greatest interest for the study of the evolutionary mechanisms that generate lexical differences between “generic populations.” But their interpretation involves quite complex problems. When two rhetorical forms or genres are lexically similar, the resemblance may be the result of a common historical origin, but it can also be due to their development in similar geographic or political environments. Nor should we forget that styles of life and cultural attitudes of an analogous nature (for example, religious dictates) can favour the increase or decrease to the point of extinction of certain lexical items or clusters of lexical items.

Why do words (and hence their frequencies and “clusterings”) vary over time and space? They do so because of stylistic innovations. Such innovation occurs very rarely, and when it happens, it persists equally rarely in a given generic population in the long run . . . From an evolutionary point of view, the mechanism of innovation is very important because it introduces new rhetorical forms . . .

. . . The evolutionary mechanism capable of changing the lexical structure of a rhetorical form or genre most swiftly is cultural selection, which favours the forms best adapted for survival to publication and circulation, or with a higher degree of influence (meaning a higher likelihood of being reproduced by others without too many changes). Cultural selection, whose action is continuous over time, having to eliminate rhetorical innovations or “mutations” that are injurious in a given cultural habitat, is the mechanism that adapts a rhetorical form to the environment that surrounds it.

Obviously, it’s not perfect. I leave it to the reader to decide its persuasive potential.

I think the biggest problem is in the handling of mutations. In biological evolution, genes mutate via chance variations during replication of their segments; these mutations can introduce innovations in an organism’s form or function. In literary evolution, however, no sharp distinction exists between a lower-scale “mutation” and the innovation it introduces. The innovation is the formal mutation. This issue arises because, in literary evolution, as in linguistic evolution, the genotype/phenotype distinction is not as obvious or strictly scaled as it is in evolutionary theory. Words are more phenotype than genotype, unless we want to get lost in an overly complex evocation of morphology and phonology.

The metaphor always breaks down somewhere, but where it works, it is, I think, highly suggestive: the idea is that we track rhetorical forms—constellations of words and their stylistic variants—across time and space, in order to see where the forms replicate and where they disappear. Attach meta-data to the texts that constitute those forms, and we will have what it takes to begin making data-driven arguments about how cultural ecology affects or does not affect cultural form.

It’s an interesting framework in which distant reading might go forward, even if explicit uses of the word “evolution” are abandoned.

“Re-purposing Data” in the Digital Humanities

Histories of science and technology provide many examples of accidental discovery. Researchers go looking for one thing and find another. Or, more often, they look for one thing, find something else but don’t realize it until someone points it out in a completely different context. The serendipitous “Eureka!” is the most exciting of all.

Take the microwave oven. Its inventor, Percy Spencer, was not trying to discover a quick, flameless way to cook food. He was working on a magnetron, a vacuum tube designed to produce electromagnetic wavelengths for short wave radar. One day, he came to work with a chocolate bar in his pocket. The wavelengths melted the candy bar. Intrigued, Spencer tried to pop popcorn with the magnetron. That worked, too. So Spencer constructed a metal box, then fed micro-waves and food into it. Voila. A radar tech discovers that a property of the magnetron can be repurposed, from creating short wavelengths for radar to creating hot dogs in 30 seconds.

Another example is the discovery of cosmic microwave background radiation, the defining piece of evidence in support of the Big Bang Theory. Wikipedia tells the story well:

By the middle of the 20th century, cosmologists had developed two different theories to explain the creation of the universe. Some supported the steady-state theory, which states that the universe has always existed and will continue to survive without noticeable change. Others believed in the Big Bang theory, which states that the universe was created in a massive explosion-like event billions of years ago (later to be determined as 13.8 billion).

Working at Bell Labs in Holmdel, New Jersey, in 1964, Arno Penzias and Robert Wilson were experimenting with a supersensitive, 6 meter (20 ft) horn antenna originally built to detect radio waves bounced off Echo balloon satellites. To measure these faint radio waves, they had to eliminate all recognizable interference from their receiver. They removed the effects of radar and radio broadcasting, and suppressed interference from the heat in the receiver itself by cooling it with liquid helium to −269 °C, only 4 K above absolute zero.

When Penzias and Wilson reduced their data they found a low, steady, mysterious noise that persisted in their receiver. This residual noise was 100 times more intense than they had expected, was evenly spread over the sky, and was present day and night. They were certain that the radiation they detected on a wavelength of 7.35 centimeters did not come from the Earth, the Sun, or our galaxy. After thoroughly checking their equipment, removing some pigeons nesting in the antenna and cleaning out the accumulated droppings, the noise remained. Both concluded that this noise was coming from outside our own galaxy—although they were not aware of any radio source that would account for it.

At that same time, Robert H. Dicke, Jim Peebles, and David Wilkinson, astrophysicists at Princeton University just 60 km (37 mi) away, were preparing to search for microwave radiation in this region of the spectrum. Dicke and his colleagues reasoned that the Big Bang must have scattered not only the matter that condensed into galaxies but also must have released a tremendous blast of radiation. With the proper instrumentation, this radiation should be detectable, albeit as microwaves, due to a massive redshift.

When a friend (Bernard F. Burke, Prof. of Physics at MIT) told Penzias about a preprint paper he had seen by Jim Peebles on the possibility of finding radiation left over from an explosion that filled the universe at the beginning of its existence, Penzias and Wilson began to realize the significance of their discovery. The characteristics of the radiation detected by Penzias and Wilson fit exactly the radiation predicted by Robert H. Dicke and his colleagues at Princeton University. Penzias called Dicke at Princeton, who immediately sent him a copy of the still-unpublished Peebles paper. Penzias read the paper and called Dicke again and invited him to Bell Labs to look at the Horn Antenna and listen to the background noise. Robert Dicke, P. J. E. Peebles, P. G. Roll and D. T. Wilkinson interpreted this radiation as a signature of the Big Bang.

Penzias and Wilson were looking for one thing for Bell Labs, found something else, thought it might have been pigeon shit, then realized they’d stumbled upon evidence directly relevant to another research project.

In the sciences, data are data, and once presented, they are there for the taking. “Repurposing data”—using data compiled for one project for your own project. In some sense, all scholars do this. Bibliographies and lit reviews signal that a piece of scholarship has built on existing scholarship. In the humanities, however, scholars are accustomed to building on whole arguments, not individual points of data. If Dicke, Peebles, and Wilkinson had been humanists, they would have asked, “How does the practice of detecting faint radio waves bounced off Echo balloon satellites relate to our work on cosmic background radiation?” Which is not necessarily the wrong question to ask, the connection might have been forged eventually, but given that everyone involved were scientists, no one posed the question that way, and I imagine it was much more natural for Penzias’ and Wilsons’ data to be removed from its  context and placed into another context. Humanists, on the other hand, are not conditioned to chop up another scholar’s argument, isolate a detail, remove it, and put it into an unrelated argument. This seems like bad form. Sources, their contexts, the nuances of their arguments are introduced in total—this is vital if you are going to use a source properly in the humanities.

Digital humanists construct arguments just like any other humanists, but rather than deploying what Rebecca Moore Howard calls “ethos-based” argumentation, DH’s typically traffic in mined and researched data—the locations of beginnings and endings in Jane Austen novels; citation counts in academic journals; metadata relating to the genders and nationalities of authors. These data always exist in the context of a specific argument made by the researcher who has compiled them, but data are more portable than ethos-based arguments, in which any one strand of thought relies on all the others. No such reliance exists, however, in data-based argumentation. In other words, an antimetabole: a data-based argument relies on the data, but the data do not rely on the argument.

A hypothetical example and a real one:

In “Style, Inc: Reflections on 7,000 Titles,” Moretti compiles a very particular set of data: the word counts of British novel titles between 1740 and 1850. He provides several graphs to document an obvious trend, that novel titles got drastically shorter throughout the 18th and 19th centuries. From these data, Moretti makes, as he usually does, a compelling argument about the literary marketplace and its effect on literary form:

As the number of new novels kept increasing, each of them had inevitably a much smaller ‘window’ of visibility on the market, and it became vital for a title to catch quickly and effectively the eye of the public. [Summary titles] were not good at that. They were good at describing a book in isolation: but when it came to standing out in a crowded marketplace, short titles were better—much easier to remember, to begin with. (187-88)

Moretti’s argument relies on his analysis of data about novel titles; his argument would be weaker (non-existent?) without the data. But now that these data have been compiled, are they useful only in the context of Moretti’s argument? Of course not. Let’s say I’m a book historian writing my dissertation on changing book and paper sizes between 1500 and 1900. Let’s say I’ve discovered (hypothetically—it’s probably not true) that smaller book sizes—duodecimos and even sextodecimos—proliferated between 1810 and 1900, relative to earlier decades in the 18th century. Now let’s say I find Moretti’s article on shortened book titles during the same period. Hmm, I think. Interesting. Never mind that “Style, Inc.” is focused on literary form, never mind that I’m writing about the materials of book history, never mind that I’m not interested in Moretti’s argument about literary form per se: Moretti’s data nevertheless might generate an interesting discussion. Maybe I’ll look at titles more closely. Maybe I can even get a whole chapter out of this—“Titles and Title Pages in relation to Book Sizes.” A serendipitous connection. A scholar in book history and a literary scholar making different but in no way opposed arguments from the same data.

Real example: I’ve just finished a paper on the construction of disciplinary boundaries in academic journals. In it, I use data from Derek Mueller’s article which counts citations in the journal College Composition and Communication. I also compile citations from other journals, focusing on citations in abstracts. But the argument I make is not quite the same as Mueller’s. In fact, I analyze my data on citations in a way that hopefully shines a new light on Mueller’s data. Both Mueller and I discover (unsurprisingly) that citations in articles and abstracts form a power law distribution. Mueller argues that the “long tail” of the citation distribution implies a “loose amalgamation” of disparate scholarly interests and that the head of the distribution represents the small canon uniting the otherwise disparate interests. I argue, however, that when we look at the entire distribution thematically, we discover that each unique citation added to the distribution—whether it ends up in the head or the long tail—may in fact be thematically connected to many other citations, whether they also be in the head or the long tail. (For example, Plato is in the head of one journal’s citation distribution, and Aristophanes is in the long tail, but a scholar’s addition of Aristophanes to the long tail does not imply scholarly divergence from the many additions of Plato. Both citations suggest unity insofar as both signal a single scholarly focus on rhetorical history.)

I re-purpose Mueller’s data but not his argument. Honestly, in my paper, I don’t spend much time at all working through the nuances of Mueller’s paper because they’re not important to mine. His data are important—they and the methods he used to compile them are the focus of my argument, which moves in a slightly different direction than Mueller’s.

To reiterate: data in the digital humanities beg to be re-purposed, taken from one context and transferred to another. All arguments rely on data, but the same data may always be useful to another argument. At the end of my paper, I write: “I have used these corpora of article abstracts to analyze disciplinary identity, but this same group of texts can be mined with other (or the same) methods to approach other research questions.” That’s the point. Are digital humanists doing this? They certainly re-purpose and evoke one another’s methods, but to date, I have not seen any papers citing, for example, Moretti’s actual maps to generate an argument not about methods but about what the maps might mean. Just because Moretti generated these geographical data does not mean he has sole ownership over their implications or their usefulness in other contexts.

There’s a limit to all this, of course. Pop-science journalism, at its worst, demonstrates the hazards of decontextualizing a data-point from a larger study and drawing all sorts of wild conclusions from it, conclusions contradicted by the context and methods of the study from which the data-point was taken. It is still necessary to analyze critically the research from which data are taken and, more importantly, the methods used to obtain them. However, if we are confident that the methods were sound and that our own argument does not contradict or over-simplify something in the original research, we can be equally confident in re-purposing the data for our own ends.

Meaning circulation in Lolita

Text networks allow you to trace the circulation of meaning within a text.

A text network analysis proceeds in the following way: a text is copied into a .txt file; it is imported into some analytic tool (I use Auto Map) in order to remove stop words and to lightly stem the text; then, using the same tool, the text—which has now been expunged of all but significant content words—is run through an algorithm that treats the content words like a network and creates a co-reference list in .csv format. What words are connected to what other words, and how often? The .csv file is then opened in a network analysis tool (I use Gephi) in order to visualize these semantic connections. Each word is visualized as a node in the network, and words that appear next to each other—within a certain word gap—appear as edges. (I used a 3-word gap below.) 

The most interesting network visualization, in my opinion, shows nodes with the highest levels of Betweenness Centrality, which measures whether or not a node is connected to other nodes that themselves have many connections; a node with high betweenness centrality will in essence be an important ‘passageway’ between communities within the network. (Here’s an excellent visual description of the concepts.)

In a text network, a word with high degree centrality is a word used in connection with myriad other words. This simply tells you that a word is used frequently in a text and in a variety of contexts (it will more or less be a productive creator of bigrams). However, a word with high betweenness centrality is a word used frequently and in conjunction with other words that also connect to other words to form community clusters. This tells you that a word is not only used frequently and not only in many contexts but also that it is used in connection with words that also do a lot of semantic work in the text. A word with high betweenness centrality is a word through which many meanings in a text circulate.

Using Auto Map and Gephi, and following a methodology similar to the one described here, I created a network of all the lexical connections within the first 10 chapters of Vladimir Nabokov’s Lolita. (View the upcoming videos in full screen; otherwise, you can’t see the nodes I’m talking about.) The results here show which words possess the highest betweenness centrality. The more betweenness centrality, the larger the node.

The results also allow us to trace all the possible connections from one word to any other word, both within individual meaning clusters and through terms with a high level of betweenness centrality. For example, the terms ‘girl’ and ‘night’ have a relatively high betweenness centrality, and they are both connected to one another through the word ‘touched’, which itself is not connected to very many clusters and thus has a low betweenness centrality.

night –> touched –> girl

(Lots of pervy pathways of meaning in Lolita.)

Visualizing all the connections in this textual network is messy. Nabakov was a master stylist, not one to use the same words too often, and certainly not in the same sentence or in the same connective pattern. The average path length in the text is 7.95. Average path length measures how many steps you need to take on average to connect two randomly selected nodes. The lower the average path length, the more connected the text. At 7.95, the first 10 chapters in Lolita are not very connected; there are 221 separate meaning clusters. Here’s the messy initial network . . .

Using Gephi’s degree range tool, I hid the most disconnected nodes, thereby ‘cleaning’ the visualization of all but the most prominent clusters and connections.

With this cleaner network, I could see a few distinct clusters, as well as those terms with high degrees of betweenness centrality, the words that act as conduits between different words and meaning clusters. They were what you’d expected: meaning in Lolita circulates through the favorite words of an enamored pederast. Nymphet, night, girl, age, eyes, hair . . .

More interesting than the overall network, however, were the various paths I found between different terms. In general, fewer than 3 paths of separation in a social network = a possible cross-influence between two nodes. In our text network, two words separated by 3 paths or fewer = a possible, latent relationship between the words, perhaps even a relationship that can be expressed in terms of influence.

For example, ‘nymphet’ led backward to ‘annabel’, which had a direct path to ‘lolita’ in one direction and to ‘death’ in the other direction.

Remember, this textual network only represents the first 10 chapters of the novel (I didn’t include the fake preface). And yet, already built into this network of lexemes from early in the novel is a clue to Humbert’s eventual demise, a great example of the intimate connection between form and function, style and plot.

Another interesting pathway was the path between ‘life’ and ‘death’. Actually, there were two pathways, one leading through ‘felt’ and another leading, oddly enough, through ‘love’ and then ‘father’.

The ‘father’, ‘love’, ‘death’ triangle is quite interesting . . . and, of course, ‘death’ leads back through ‘felt’ to ‘annabel’, the first nymphet in Humbert’s life.

Finally, two important terms in the network are quite disconnected: ‘nymphet’ and ‘girl’. Which is exactly what we should expect. Humbert goes to great lengths to separate the one from the other, and textually, it’s difficult to trace a lexemic path from one to the other. (note: at the end of this video, I highlight the word ‘fruit’, which is only connected to ‘table’ and ‘set’. Nabokov apparently declines to use any sort of forbidden fruit metaphor during the first ten chapters of the novel; ‘fruit’ never connects to the pervy words or meaning clusters.)

Even this short analysis has given me some interesting things to discuss if I were actually writing a dissertation on Lolita. The meaning circulation of ‘lolita’, ‘annabel’, and ‘death’ through the conduit ‘nymphet’ would be worth analyzing in more detail, especially considering that this circulation occurs so early in the novel.