As you’ll see if you download it, this is not a general digital humanities course. At Urbana-Champaign, John Unsworth has been teaching an introduction to digital humanities in the Graduate School of Library and Information Science, and there’s no way I could hope to replicate his breadth of knowledge. Instead I’ve focused on literary and historical applications of text mining, because that’s an area where I feel I can teach skills that a wide range of humanities graduate students will find immediately useful.
I realize the choice of focus may seem odd, since text mining is a relatively controversial subfield of DH, and a technically challenging one. There’s no way to duck the technical challenge: I am going to try to teach enough coding (using R) to empower students to define their own questions and visualize their own results. But I don’t think controversies about quantification need to be a problem, since I approach text mining largely as a discovery strategy. I hope it will turn up insights and clues that students find useful, without necessarily compelling them to add a lot of numbers or graphs to their arguments.
The “tools” and “theory” in the title of the course are not meant to be pitted against each other. The title instead flags a working assumption that practice and theory are fused: our interpretive theories are already shaped by the social/technical infrastructure we use to find and read texts, so reflectively reshaping that infrastructure is a way of “doing theory.”
My reaction to Stanley Fish’s third column on digital humanities was at first so negative that I thought it not worth writing about. But in the light of morning, there is something here worth discussing. Fish raises a neglected issue that I (and a bunch of other people cited at the end of this post) have been trying to foreground: the role of discovery in the humanities. He raises the issue symptomatically, by suppressing it, but the problem is too important to let that slide.
Fish argues, in essence, that digital humanists let the data suggest hypotheses for them instead of framing hypotheses that are then tested against evidence.
The usual way of doing this is illustrated by my example: I began with a substantive interpretive proposition … and, within the guiding light, indeed searchlight, of that proposition I noticed a pattern that could, I thought be correlated with it. I then elaborated the correlation.
The direction of my inferences is critical: first the interpretive hypothesis and then the formal pattern, which attains the status of noticeability only because an interpretation already in place is picking it out.
The direction is the reverse in the digital humanities: first you run the numbers, and then you see if they prompt an interpretive hypothesis. The method, if it can be called that, is dictated by the capability of the tool.
The underlying element of truth here is that all researchers — humanists and scientists alike — do need to separate the process of discovering a hypothesis from the process of testing it. Otherwise you run into what we unreflecting empiricists call “the problem of data dredging.” If you simply sweep a net through an ocean of data, and frame a conclusion based on whatever you catch, you’re not properly testing anything, because you’re implicitly testing an infinite number of hypotheses that are left unstated — and the significance of any single test is reduced when it’s run as part of a large battery.
That’s true, but it’s also a problem that people who do data mining are quite self-conscious about. It’s why I never stop linking to this xkcd comic about “significance.” And it’s why Matt Wilkens (mistargeted by Fish as an emblem of this interpretive sin) goes through a deliberately iterative process of first framing hypotheses about nineteenth-century geographical imagination and then testing them. (For instance, after noticing that certain states seem especially prominent in 19c American fiction, he tests whether this remains true after you compensate for differences in population size, and then proposes a pair of hypotheses that he suggests will need to be evaluated against additional “test cases.”)
More importantly, Fish profoundly misrepresents his own (traditional) interpretive procedure by pretending that the act of interpretation is wholly contained in a single encounter with evidence. On his account we normally begin with a hypothesis (which seems to have sprung, like Sin, fully-formed from our head), and test it against a single sentence.
In reality, of course, our “interpretive proposition” is often suggested by the same evidence that confirms it. Or — more commonly — we derive a hypothesis from one example, and then read patiently through dozens of books until we have gathered enough confirming evidence to write a chapter. This process runs into a different interpretive fallacy: if you keep testing a hypothesis until you’ve confirmed it, you’re not testing it at all. And it’s a bit worse than that, because in practice what we do now is go to a full-text search engine and search for terms that would go together if our assumptions were correct. (In the example Fish offers, this might be “bishops” and “presbyters.”) If you find three sentences where those terms coincide, you’ve got more than enough evidence to prop up an argument, using our richly humanistic (cough, anecdotal) conception of evidence. And of course a full-text search engine can find you three examples of just about anything. But we don’t have to worry about this, because search engines are not tools that dictate a method; they are transparent extensions of our interpretive sensibility.
The basic mistake that Fish is making is this: he pretends that humanists have no discovery process at all. For Fish, the interpretive act is always fully contained in an encounter with a single piece of evidence. How your “interpretive proposition” got framed in the first place is a matter of no consequence: some readers are just fortunate to have propositions that turn out to be correct. Fish is not alone in this idealized model of interpretation; it’s widespread among humanists.
Fish is resisting the assistance of digital techniques, not because they would impose scientism on the humanities, but because they would force us to acknowledge that our ideas do after all come from somewhere — whether a search engine or a commonplace book. But as Peter Stallybrass eloquently argued five years ago in PMLA (h/t Mark Sample) the process of discovery has always been collaborative, and has long — at least since early modernity — been embodied in specific textual technologies.
Stallybrass, Peter. “Against Thinking.” PMLA 122.5 (2007): 1580-1587.
Wilkens, Matthew. “Geolocation Extraction and Mapping of Nineteenth-Century U.S. Fiction.” DHCS 2011.
On the process of embodied play that generates ideas, see also Stephen Ramsay’s book Reading Machines (University of Illinois Press, 2011).
Fish argues that digital humanists’ insistence on the networked character of human communication (or even human identity) makes them a) postmodern, b) theological, in the sense that they’re promising a transcendence of individual mortality, and c) political in an explicitly leftist way. In making these points, he cites about 2.5% of the people in my Twitter stream, which is one reason why I like the column.
The main reason I like it, though, is that it raises the bar for stylistic slipperiness in the pages of the NYT. Fish begins the column by posing as someone with a firm belief in the stability of the text, and in authorial identity. He says that he believes in these as strongly, in fact, as the critic Morris Zapp. This is pretty delicious, given that Zapp is a fictional character notoriously modeled on Stanley Fish. He can hardly function as an emblem of stable authorial identity … though he might well emblematize the immortal alter-ego that writing has always made possible. I’m reminded of the “laugh that wasn’t laughter” at the end of Neuromancer.
Which brings me to the only place in the column where I do feel dissed. Fish thinks humanists promoting DH will be shocked by the notion that enthusiasm for the web involves a religious transcendence of mortality. Come on — we’ve read @GreatDismal. Moreover, a lot of us have read Emile Durkheim on the religious character of all social feeling, or Carl Becker on the Enlightenment’s secular faith in posterity. Just about all forms of reflection on history and writing promise a transcendence of individual identity.
I enjoyed this column so much that I’m hoping the third installment (about digital analysis of “aesthetic works”) will be equally thoughtful and slippery. I’m rooting for Fish to resist the magnetic pull of formulations like “computers will never …” and “merely counting words can never ….” But those binary assumptions are hard to resist: I’m going to be wracked with suspense.
UPDATE Jan 23rd. This really isn’t worth a blog post. But I should just briefly register my disappointment in Fish’s third column. It’s sophistry, and not even sophistry of an interesting kind. Once you say “excluded middle fallacy founded on willful misreading of two examples,” you’ve pretty much done all that needs to be done with it. Too bad.
Giving a talk this morning at the MLA. There are two main arguments:
1) The first one will be familiar if you’ve read my blog. I suggest that the boundary between “text mining” and conventional literary research is far fuzzier than people realize. There appears to be a boundary only because literary scholars are pretty unreflective about the way we’re currently using full-text search. I’m going to press this point in detail, because it’s not just a metaphor: to produce a simple but useful topic-modeling algorithm, all you have to do is take a search engine and run it backwards.
2) The second argument is newer; I don’t think I’ve blogged about it yet. I’m going to present topic modeling as a useful bridge between “distant” and “close” reading. I’ve found that I often learn most about a genre by modeling it as part of a larger collection that includes many other genres. In that context, a topic-modeling algorithm can highlight peculiar convergences of themes that characterize the genre relative to its contemporary backdrop.
This is distant reading, in the sense that it requires a large collection. But it’s also close reading, in the sense that it’s designed to reveal subtle formal principles that shape individual works, and that might otherwise elude us.
In responding to Stanley Fish last week, I tried to acknowledge that the “digital humanities,” in spite of their name, are not centrally about numbers. The movement is very broad, and at the broadest level, it probably has more to do with networked communication than it does with quantitative analysis.
The older tradition of “humanities computing” — which was about numbers — has been absorbed into this larger movement. But it’s definitely the part of DH that humanists are least comfortable with, and it often has to apologize for itself. So, for instance, I’ve spent much of the last year reminding humanists that they’re already using quantitative text mining in the form of search engines — so it can’t be that scary.* Kathleen Fitzpatrick recently wrote a post suggesting that “one key role for a ‘worldly’ digital humanities may well be helping to break contemporary US culture of its unthinking association of numbers with verifiable reality….” Stephen Ramsay’s Reading Machines manages to call for an “algorithmic criticism” while at the same time suggesting that humanists will use numbers in ways that are altogether different from the way scientists use them (or at least different from “scientism,” an admittedly ambiguous term).
I think all three of us (Stephen, Kathleen, and myself) are making strategically necessary moves. Because if you tell humanists that we do (also) need to use numbers the way scientists use them, your colleagues are going to mutter about naïve quests for certainty, shake their heads, and stop listening. So digital humanists are rhetorically required to construct positivist scapegoats who get hypothetically chased from our villages before we can tell people about the exciting new kinds of analysis that are becoming possible. And, to be clear, I think the people I’ve cited (including me) are doing that in fair and responsible ways.
However, I’m in an “eppur si muove” mood this morning, so I’m going to forget strategy for a second and call things the way I see them. <Begin Galilean outburst>
In reality, scientists are not naïve about the relationship between numbers and certainty, because they spend a lot of time thinking about statistics. Statistics is the science of uncertainty, and it insists — as forcefully as any literary theorist could — that every claim comes accompanied by a specific kind of ignorance. Once you accept that, you can stop looking for absolute knowledge, and instead reason concretely about your own relative uncertainty in a given instance. I think humanists’ unfamiliarity with this idea may explain why our critiques of data mining so often taken the form of pointing to a small error buried somewhere in the data: unfamiliarity with statistics forces us to fall back on a black-and-white model of truth, where the introduction of any uncertainty vitiates everything.
Moreover, the branch of statistics most relevant to text mining (Bayesian inference) is amazingly, almost bizarrely willing to incorporate subjective belief into its definition of knowledge. It insists that definitions of probability have to depend not only on observed evidence, but on the “prior probabilities” that we expected before we saw the evidence. If humanists were more familiar with Bayesian statistics, I think it would blow a lot of minds.
I know the line about “lies, damn lies, and so on,” and it’s certainly true that statistics can be abused, as this classic xkcd comic shows. But everything can be abused. The remedy for bad verbal argument is not to “remember that speech should stay in its proper sphere” — it’s to speak better and more critically. Similarly, the remedy for bad quantitative argument is not “remember that numbers have to stay in their proper sphere”; it’s to learn statistics and reason more critically.
None of this is to say that we can simply borrow tools or methods from scientists unchanged. The humanities have a lot to add — especially when it comes to the social and historical character of human behavior. I think there are fascinating advances taking place in data science right now. But when you take apart the analytic tools that computer scientists have designed, you often find that they’re based on specific mistaken assumptions about the social character of language. For instance, there’s a method called “Topics over Time” that I want to use to identify trends in the written record (Wang and McCallum, 2006). The people who designed it have done really impressive work. But if a humanist takes apart the algorithm underlying this method, they will find that it assumes that every trend can be characterized as a smooth curve called a “Beta distribution.” Whereas in fact, humanists have evidence that the historical trajectory of a topic is often more complex than that, in ways that really matter. So before I can use this tool, I’m going to have to fix that part of the method.
But this is a problem that can be fixed, in large part, by fixing the numbers. Humanists have a real contribution to make to the science of data mining, but it’s a contribution that can be embodied in specific analytic insights: it’s not just to hover over the field like the ghost of Ben Kenobi and warn it about hubris.
For related thoughts, somewhat more temperate than the outburst above, see this excellent comment by Matthew Wilkens, responding to a critique of his work by Jeremy Rosen.
* I credit Ben Schmidt for this insight so often that regular readers are probably bored. But for the record: it comes from him.