Structuralist Methods in a Post-Structuralist Humanities

The topic of this conference (going on now!) at Utrecht University raises an issue similar to the one raised in my article at LSE’s Impact Blog: DH’ists have been brilliant at mining data but not always so brilliant at pooling data to address the traditional questions and theories that interest humanists. Here’s the conference description (it focuses specifically on DH and history):

Across Europe, there has been much focus on digitizing historical collections and on developing digital tools to take advantage of those collections. What has been lacking, however, is a discussion of how the research results provided by such tools should be used as a part of historical research projects. Although many developers have solicited input from researchers, discussion between historians has been thus far limited.

The workshop seeks to explore how results of digital research should be used in historical research and to address questions about the validity of digitally mined evidence and its interpretation.

And here’s what I said in my Impact Blog article, using as an example my own personal hero’s research in literary geography:

[Digital humanists] certainly re-purpose and evoke one another’s methods, but to date, I have not seen many 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.

I realize now that the problem is still one of method—or, more precisely, of method incompatibility. And the conference statement above gets to the heart of it.

Mining results with quantitative techniques is ultimately just data gathering; the next and more important step is to build theories and answer questions with that data. The problem is, in the humanities, that moving from data gathering to theory building forces the researcher to move between two seemingly incommensurable ways of working. Quantitative data mining is based on strict structuralist principles, requiring categorization and sometimes inflexible ontologies; humanistic theories about history or language, on the other hand, are almost always post-structuralist in their orientation. Even if we’re not talking Foucault or Derrida, the tendency in the humanities is to build theories that reject empirical readings of the world that rely on strict categorization. The 21st century humanistic move par excellence is to uncover the influence of “socially constructed” categories on one’s worldview (or one’s experimental results).

On Twitter, Melvin Wevers brings up the possibility of a “post-structuralist corpus linguistics.” To which James Baker and I replied that that might be a contradiction in terms. To my knowledge, there is no corpus project in existence that could be said to enact post-structuralist principles in any meaningful way. Such a project would require a complete overhaul of corpus technology from the ground up.

So where does that leave the digital humanities when it comes to the sorts of questions that got most of us interested in the humanities in the first place? Is DH condemned forever to gather interesting data without ever building (or challenging) theories from that data? Is it too much of an unnatural vivisection to insert structural, quantitative methods into a post-structuralist humanities?

James Baker throws an historical light on the question. When I said that post-structuralism and corpus linguistics are fundamentally incommensurable, he replied with the following point:

And he suggested that in his own work, he tries to follow this historical development:

Structuralism/post-structuralism exists (or should exist) in dialectical tension. The latter is a real historical response to the former. It makes sense, then, to enact this tension in DH research. Start out as a positivist, end as a critical theorist, then go back around in a recursive process. This is probably what anyone working with DH methods probably does already. I think Baker’s point is that my “problem” posed above (structuralist methods in a post-structuralist humanities) isn’t so much a problem as a tension we need to be comfortable living with.

Not all humanistic questions or theories can be meaningfully tackled with structuralist methods, but some can. Perhaps a first step toward enacting the structuralist/post-structuralist dialectical tension in research is to discuss principles regarding which topics are or are not “fair game” for DH methods. Another step is going to be for skeptical peer reviewers not to balk at structuralist methods by subtly trying to remove them with calls for more “nuance.” Searching out the nuances of an argument—refining it—is the job of multiple researchers across years of coordinated effort. Knee-jerk post-structuralist critiques (or requests for an author to put them in her article) are unhelpful when a researcher has consciously chosen to utilize structuralist methods.

Some questions about centrality measurements in text networks


This .gif alternates between a text network calculated for betweenness centrality (smaller nodes overall) and one calculated for degree centrality (larger nodes). It’s normal to discover that most nodes in a network possess higher degree than betweenness centrality. However, in the context of human language, what precisely is signified by this variation? And is it significant?

Another way of posing the question is to ask what exactly one discovers about a string of words by applying centrality measurements to each word as though it were a node in a network, with edges between words to the right or left of it. The networks in the .gif visualize variation between two centrality measurements, but there are dozens of others that might have been employed. Which centrality measurements—if any—are best suited for textual analysis? When centrality measurements require the setting of parameters, what should those parameters be, and are they dependent on text size? And ultimately, what literary or rhetorical concept is “centrality” a proxy for? The mathematical core of a centrality measurement is a distance matrix, so what do we learn about a text when calculating word proximity (and frequency of proximity, if calculating edge weight)? Do we learn anything that would have any relevance to anyone since the New Critics?

It is not my goal (yet) to answer these questions but merely to point out that they need answers. DH researchers using networks need to come to terms with the linear algebra that ultimately generates them. Although a positive correlation should theoretically exist between different centrality measurements, differences do remain, and knowing which measurement to utilize in which case should be a matter of critical debate. For those using text networks, a robust defense of network application in general is needed. What is gained by thinking about text as a word network?

In an ideal case, of course, the language of social network theory transfers remarkably well to the language of rhetoric and semantics. Here is Linton C. Freeman discussing the notion of centrality in its most basic form:

Although it has never been explicitly stated, one general intuitive theme seems to have run through all the earlier thinking about point centrality in social networks: the point at the center of a star or the hub of a wheel, like that shown in Figure 2, is the most central possible position. A person located in the center of a star is universally assumed to be structurally more central than any other person in any other position in any other network of similar size. On the face of it, this intuition seems to be natural enough. The center of a star does appear to be in some sort of special position with respect to the overall structure. The problem is, however, to determine the way or ways in which such a position is structurally unique.

Previous attempts to grapple with this problem have come up with three distinct structural properties that are uniquely possessed by the center of a star. That position has the maximum possible degree; it falls on the geodesics between the largest possible number of other points and, since it is located at the minimum distance from all other points, it is maximally close to them. Since these are all structural properties of the center of a star, they compete as the defining property of centrality. All measures have been based more or less directly on one or another of them . . .

Addressing the notions of degree and betweenness centrality, Freeman says the following:

With respect to communication, a point with relatively high degree is somehow “in the thick of things”. We can speculate, therefore, that writers who have defined point centrality in terms of degree are responding to the visibility or the potential for activity in communication of such points.

As the process of communication goes on in a social network, a person who is in a position that permits direct contact with many others should begin to see himself and be seen by those others as a major channel of information. In some sense he is a focal point of communication, at least with respect to the others with whom he is in contact, and he is likely to develop a sense of being in the mainstream of information flow in the network.

At the opposite extreme is a point of low degree. The occupant of such a position is likely to come to see himself and to be seen by others as peripheral. His position isolates him from direct involvement with most of the others in the network and cuts him off from active participation in the ongoing communication process.

The “potential” for a node’s “activity in communication” . . . A “position that permits direct contact” between nodes . . . A “major channel of information” or “focal point of communication” that is “in the mainstream of information flow.” If the nodes we are talking about are words in a text, then it is straightforward (I think) to re-orient our mental model and think in terms of semantic construction rather than interpersonal communication. In other posts, I have attempted to adopt degree and betweenness centrality to a discussion of language by writing that, in a textual network, a word with high degree centrality is essentially a productive creator of bigrams but not a pathway of meaning. A word with high betweenness centrality, on the other hand, is a pathway of meaning: it is a word whose significations potentially slip as it is used first in this and next in that context in a text.

Degree and betweenness centrality—in this ideal formation—are therefore equally interesting measurements of centrality in a text network. Each points you toward interesting aspects of a text’s word usage.

However, most text networks are much messier than the preceding description would lead you to believe. Freeman, again, on the reality of calculating something as seemingly basic as betweenness centrality:

Determining betweenness is simple and straightforward when only one geodesic connects each pair of points, as in the example above. There, the central point can more or less completely control communication between pairs of others. But when there are several geodesics connecting a pair of points, the situation becomes more complicated. A point that falls on some but not all of the geodesics connecting a pair of others has a more limited potential for control.

In the graph of Figure 4, there are two geodesics linking pi with p3, one EJ~U p2 and one via p4. Thus, neither p2 nor p4 is strictly between p, and p3 and neither can control their communication. Both, however, have some potential for control.


Calculating betweenness centrality in this (still simple) case requires recourse to probabilities. A probabilistic centrality measure is not necessarily less valuable; however, the concept should give you an idea of the complexities involved in something as ostensibly straightforward as determining which nodes in a network are most “central.” Put into the context of a text network, a lot of intellectual muscle would need to be exerted to convert such a probability measurement into the language of rhetoric and literature (then again, as I write that . . .).

As I said, there is reading to be done, mathematical concepts to comprehend, and debates to be had. And ultimately, what we are after perhaps isn’t centrality measurements at all but metrics for node (word) influence. For example, if we assume (as I think we can) that betweenness centrality is a better metric of node influence than degree centrality, then the .gif above clearly demonstrates that degree centrality may be a relatively worthless metric—it gives you a skewed sense of which words exert the most control over a text. What’s more, node influence is a concept sensitive to scale. Though centrality measurements may inform us about influential nodes across a whole network, they may underestimate the local or temporal influence of less central nodes. Centrality likely correlates with node influence but I doubt it is determinative in all cases. Accessing text (from both a writer’s and a reader’s perspective) is ultimately a word-by-word or phrase-by-phrase phenomenon, so a robust text network analysis needs to consider local influence. A meeting of network analysis and reader response theory may be in order.  Perhaps we are even wrong to expunge functional words from network analysis. As Franco Moretti has demonstrated, analysis of words as seemingly disposable as ‘of’ and ‘the’ can lead to surprising conclusions. We leave these words out of text networks simply because they create messy, spaghetti-monster visualizations. The underlying math, however, will likely be more informative, once we learn how to read it.


This is in response to Collin Brooke, who asked for some lists of 5.

5 Books On My Desk

  1. The Bourgeois, Franco Moretti.
  2. In the Footsteps of Genghis Khan, John DeFrancis.
  3. Warriors of the Cloisters: The Central Asian Origins of Science in the Medieval World, Christopher Beckwith.
  4. Medieval Rhetoric: A Select Bibliography, James J. Murphy
  5. The Invaders, Pat Shipman

5 Most Played Songs in my iTunes

  1. All Night Long, Lionel Richie
  2. We Are All We Need, Above and Beyond
  3. In My Memory, Tiesto
  4. Two Tickets to Paradise, Eddie Money
  5. We Built This City, Starship

5 Toppings That I Just Put On My Frozen Yogurt

  1. Peanut butter cups
  2. Chocolate chips
  3. M&Ms
  4. cookie dough
  5. whipped cream

5 Alcoholic Beverages In The Kitchen

  1. Bud Lite Lime
  2. Jose Cuervo Silver
  3. Triple-sec
  4. E&J Brandy
  5. Fireball

5 TV Shows on Netflix Instant Que

  1. Mad Men
  2. Human Planet
  3. Don’t Trust the B—- In Apartment 23
  4. Wild India
  5. Blue Planet

Some Quick Text Mining of the 2015 CCCC Program

During CCCC last week, Freddie deBoer made a couple comments about the conference: first, that there weren’t as many panels on the actual work of teaching writing compared to panels on sexier topics, like [insert stereotypical humanities topic here]; and second, that not much empirical research was being presented at the conference.

Testing these claims isn’t easy, but as a first stab, here’s a list of the most frequent unigrams and bigrams in the conference’s full list of presentation titles, as found in the official program. Make of these lists what you will. It’s pretty obvious to me that the conference wasn’t bursting at the seams with quantitative data. Sure, research appears at the head of the distribution, but I’ll leave it to you to concordance the word and figure out how often it denotes empirical research into writers while writing.

Then again, big data was a relatively popular term this year. It was used in titles more often than case studies, though case studies was used more often than digital humanities.

To Freddie’s point, the word empirical only appears 11 times in the CCCC program; the word essay appears only 16 times. Is it therefore fair to say there weren’t many empirical studies on essay writing presented this year? Maybe. Maybe not.


One way to get a flavor for the contexts and connotations of individual words and bigrams is of course to create a text network. I’ve begun to think of text networks as visual concordances.

Here is a text network of the tokens writing, write, writer, writers, writing_courses, classroom, and classrooms in the CCCC program. One thing to notice here is that each of these words is semantically related, but in the panel and presentation titles, they exist in clusters of relatively unrelated words. I had expected to discover a messy, overlapping network with these terms, but they’re rather distinct, as judged by the company they keep in the CCCC program. Even the singular and plural forms of the same noun  (e.g., from classroom to classrooms, writer to writers) form distinct clusters.


In relation to Freddie’s point, this network demonstrates that words or bigrams that are prima facie good proxies for “teaching writing” often do lead us to presentations that are pedagogical in nature. However, just as often, they lead us to presentations that are only tangentially or not at all related to the teaching of writing and to the empirical study of writers while writing.

Thus, writer forms a cluster with FYC, student, and reader but also with identity, ownership, and virtual. The same thing occurs with the other terms, though writing by far occurs alongside the most diverse range of lexical items.





This is about as much work as I’m interested in doing on the CCCC program for now. In my last post, I put a download link for a .doc version of the program, for anyone interested in doing a more thorough analysis, whether to test Freddie’s claims or to test your own ideas about the field’s zeitgeist.

However, it’s always important to keep in mind that a conference program might tell us more about the influence of conference themes than about the field itself.

ADDED: Here is a list of all names listed at the end of the CCCC program (CCCCProgramNames). Problem is, it’s a list of the FIRST and LAST names, with each given its own entry. If someone is inclined, they can go through this list and delete the last names, which will leave you with a file that can be run through a Gender Recognition algorithm, to see what the gender split of CCCC presenters was.

University representation at CCCC

Here’s a list of the universities and colleges best represented at the 2015 CCCC conference. I used NLTK to locate named entities in the CCCC program, so the graph simply represents a raw count of each time a university’s name appears in the program. Some counts might be inflated, but in general, each time a school is named = a panel with a representative from that school.

The graph shows only those schools that were named at least 10 times in the program (i.e., the schools that had at least 10 individual panels). Even in this truncated list, Michigan State dominates. Explanations for this gross inequality in representation are welcome in the comments.


Program (in .docx form because WordPress doesn’t allow .txt files)

All Your Data Are Belong To Us

In the blink of an eye, sci-fi dystopia becomes reality becomes the reality we take for granted becomes the legally enshrined status quo:

“One of our top priorities in Congress must be to promote the sharing of cyber threat data among the private sector and the federal government to defend against cyberattacks and encourage better coordination,” said Carper, ranking member of the Senate Homeland Security and Governmental Affairs Committee.

Of course, the pols are promising that data analyzed by the state will remain nameless:

The measure — known as the Cyber Threat Intelligence Sharing Act — would give companies legal liability protections when sharing cyber threat data with the DHS’s cyber info hub, known as the National Cybersecurity and Communications Integration Center (NCCIC). Companies would have to make “reasonable efforts” to remove personally identifiable information before sharing any data.

The bill also lays out a rubric for how the NCCIC can share that data with other federal agencies, requiring it to minimize identifying information and limiting government uses for the data. Transparency reports and a five-year sunset clause would attempt to ensure the program maintains its civil liberties protections and effectiveness.

Obama seems to suggest that third-party “cyber-info hubs”—some strange vivisection of private and public power—will be in charge of de-personalizing data in between Facebook and the NSA or DHS:

These industry organizations, known as Information Sharing and Analysis Organizations (ISAOs), don’t yet exist, and the White House’s legislative proposal was short on details. It left some wondering what exactly the administration was suggesting.

In the executive order coming Friday, the White House will clarify that it envisions ISAOs as membership organizations or single companies “that share information across a region or in response to a specific emerging cyber threat,” the administration said.

Already existing industry-specific cyber info hubs can qualify as ISAOs, but will be encouraged to adopt a set of voluntary security and privacy protocols that would apply to all such information-sharing centers. The executive order will direct DHS to create those protocols for all ISAOs.

These protocols will let companies “look at [an ISAO] and make judgments about whether those are good organizations and will be beneficial to them and also protect their information properly,” Daniel said.

In theory, separating powers or multiplying agencies accords with the vision of the men who wrote the Federalist Papers, the idea being to make power so diffuse that no individual, branch, or agency can do much harm on its own. However, as Yogi Berra said, “In theory there is no difference between theory and practice, but in practice there is.” Mark Zuckerberg and a few other CEOs know the difference, too. They decided not to attend Obama’s “cyber defense” summit in Silicon Valley last week.

The attacks on Target, Sony, and Home Depot (the attacks invoked by the state to prove the need for more state oversight) are criminal matters, to be sure, and since private companies can’t arrest people, the state will need to get involved somehow. But theft in the private sector is not a new thing. When a Target store is robbed, someone calls the police. No one suggests that every Target in the nation should have its own dedicated police officer monitoring the store 24/7. So why does the state need a massive data sharing program with the private sector? It’s the digital equivalent of putting police officers in every aisle of every Target store in the nation—which is likely the whole point.

Target, of course, does monitor every aisle in each of its stores 24/7. But this is a private, internal decision, and the information captured by closed circuit cameras is shared with the state only after a crime been committed. There is no room of men watching these tapes, no IT army paid to track Target movements on a massive scale, to determine who is a possible threat, to mark and file away even the smallest infraction on the chance that it is needed to make a case against someone at a later date.

What Obama and the DHS are suggesting is that the state should do exactly that: to enter every private digital space and erect its own closed circuit cameras, so that men in suits can monitor movement in these spaces whether a crime has been committed or not. (State agencies are already doing it, of course, but now the Obama Administration is attempting to increase the state’s reach and to enshrine the practice in law.)

“As long as you aren’t doing anything wrong, what do you care?”

In the short term, that’s a practical answer. In the future, however, a state-run system of closed circuit cameras watching digital space 24/7 may not always be used for justified criminal prosecution.

The next great technological revolution, in my view, will be the creation of an entirely new internet protocol suite that enables some semblance of truly “invisible” networking, or perhaps the widespread adoption of personal cloud computing. The idea will be to exit the glare of the watchers.

Hindi 101

I’m taking Hindi 101 this semester. The Devangari script feels mildly ornate in my hand compared to the angularity of alphabets descended from the Phoenician script (including the English alphabet), but it is quite lovely and not as challenging as I had imagined. It is still an alphabet, after all, with a much closer sound-grapheme correspondence than one finds in English, where each letter—particularly vowels—can correspond to multiple phonemes. (English grammar is absurdly simple compared to all other major languages, but our spelling system must be a nightmare for foreign learners. There’s something to be said for language academies that control the drift between pronunciation and spelling.) Devanagari does, however, omit some vowel sounds and uses secondary or “dependent” vowel forms in most contexts, so it has something of the syllabary about it. In fact, the biggest mistake I make in class is to confuse two dependent vowels,  ी and  ो. The former is long “ee”, the latter is “o”, but in certain fonts (including my own handwriting), they look nearly identical.

The script’s biconsonantal conjuncts are mostly intuitive, though a few bizarre ones need to be memorized as separate graphemes. We have conjuncts in English, but I believe they are a relatively new innovation with limited usage. One example is the city logo of Huntington Beach, California. Hindi has a lot of these, and they are quite common.


An English biconsonantal conjunct.

Apart from learning a new script, the most enjoyable part of Hindi class has been coming across Romance or Germanic cognates. At an intellectual level, I know and have long known that Hindi and English, both Indo-European languages, share a genetic ancestry, which means that at some point in the distant past all Indo-European speakers spoke the same language. It’s easy to get a handle on the concept when talking about Romance languages: Spanish, Italian, and French all used to be Latin. There, we have a well documented history, stretching back through the Renaissance and middle ages to the familiar  world of Rome. However, when it comes to Proto Indo-European, we are faced with a deeper and wider canyon of time and an ancient world that is mostly unknown to us. The PIE speakers were probably living in the Pontic-Caspian steppe lands, but some evidence suggests that they may have been living in the greater Anatolian region; perhaps the most direct descendants of Proto Indo-Europeans are today’s Armenians, Turks, and Persians. They apparently kicked ass and took names because Indo European now stretches from the Pacific to the Indian Oceans.

But whoever they were, the PIE speakers are remote in a way that the Romans or Germanic tribes are not. Yet while doing my Hindi homework, every now and again I come across a word that clearly indicates the ancient linguistic (and genetic) connectedness between the Romans, the Germans, and the Hindi speakers. Kamiz for shirt; mez for table; kamra for room; mata for mother; pita for father; nam for name; darvaza for door . . . In Hindi class, when I say a word out loud that is clearly related to a European word, I am intoning sounds close to the ones that came from the lips of those ancient Indo-Europeans before they split eastward and westward to conquer Eurasia. To language nerds like me, it’s a chilling sensation.