Not Enough Perspectives, Pt. 1

Right now DH is all texts, but not enough perspectives. –Andrew Piper

Summary: Digital Humanities suffers from a lack of perspectives in two ways: we need to focus more on the perspectives of those who interact with the cultural objects we study, and we need more outside academic perspectives. In Part 1, I cover Russian Formalism, questions of validity, and what perspective we bring to our studies. In Part 2, 1 I call for pulling inspiration from even more disciplines, and for the adoption and exploration of three new-to-DH concepts: Appreciability, Agreement, and Appropriateness. These three terms will help tease apart competing notions of validity.


Let’s begin with the century-old Russian Formalism, because why not? 2 Syuzhet, in that context, is juxtaposed against fabula. Syuzhet is a story’s order, structure, or narrative framework, whereas fabula is the underlying fictional reality of the world. Fabula is the story the author wants to get across, and syuzhet is the way she decides to tell it.

It turns out elements of Russian Formalism are resurfacing across the digital humanities, enough so that there’s an upcoming Stanford workshop on DH & Russian Formalism, and even I co-authored a piece that draws on work of Russian formalists. Syuzhet itself has a new meaning in the context of digital humanities: it’s a piece of code that chews books and spits out plot structures.

You may have noticed a fascinating discussion developing recently on statistical analysis of plot arcs in novels using sentiment analysis. A lot of buzz especially has revolved around Matt Jockers and Annie Swafford, and the discussion has bled into larger academia and inspired 246 (and counting) comments on reddit. Eileen Clancy has written a two-part broad link summary (I & II).

From Jockers' first post describing his method of deriving plot structure from running sentiment analysis on novels.
From Jockers’ first post describing his method of deriving plot structure from running sentiment analysis on novels.

The idea of deriving plot arcs from sentiment analysis has proven controversial on a number of fronts, and I encourage those interested to read through the links to learn more. The discussion I’ll point to here centers around “validity“, a word being used differently by different voices in the conversation. These include:

  • Do sentiment analysis algorithms agree with one another enough to be considered valid?
  • Do sentiment analysis results agree with humans performing the same task enough to be considered valid?
  • Is Jockers’ instantiation of aggregate sentiment analysis validly measuring anything besides random fluctuations?
  • Is aggregate sentiment analysis, by human or machine, a valid method for revealing plot arcs?
  • If aggregate sentiment analysis finds common but distinct patterns and they don’t seem to map onto plot arcs, can they still be valid measurements of anything at all?
  • Can a subjective concept, whether measured by people or machines, actually be considered invalid or valid?

The list goes on. I contributed to a Twitter discussion on the topic a few weeks back. Most recently, Andrew Piper wrote a blog post around validity in this discussion.

Hermeneutics of DH, from Piper's blog.
Hermeneutics of DH, from Piper’s blog.

In this particular iteration of the discussion, validity implies a connection between the algorithm’s results and some interpretive consensus among experts. Piper points out that consensus doesn’t yet exist, because:

We have the novel data, but not the reader data. Right now DH is all texts, but not enough perspectives.

And he’s right. So far, DH seems to focus its scaling up efforts on the written word, rather than the read word.

This doesn’t mean we’ve ignored studying large-scale reception. In fact, I’m about to argue that reception is built into our large corpora text analyses, even though it wasn’t by design. To do so, I’ll discuss the tension between studying what gets written and what gets read through distant reading.

The Great Unread

The Great Unread is a phrase popularized by Franco Moretti 3 to indicate the lost literary canon. In his own words:

[…] the “lost best-sellers” of Victorian Britain: idiosyncratic works, whose staggering short-term success (and long-term failure) requires an explanation in their own terms.

The phrase has since become synonymous with large text databases like Google Books or HathiTrust, and is used in concert with distant reading to set digital literary history apart from its analog counterpart. Distant reading The Great Unread, it’s argued,

significantly increase[s] the researcher’s ability to discuss aspects of influence and the development of intellectual movements across a broader swath of the literary landscape. –Tangherlini & Leonard

Which is awesome. As I understand it, literary history, like history in general, suffers from an exemplar problem. Researchers take a few famous (canonical) books, assume they’re a decent (albeit shining) example of their literary place and period, and then make claims about culture, art, and so forth based on those novels which are available.

Matthew Lincoln raised this point the other day, as did Matthew Wilkins in his recent article on DH in the study of literature and culture. Essentially, both distant- and close-readers make part-to-whole generalized inferences, but the process of distant reading forces those generalizations to become formal and explicit. And hopefully, by looking at The Great Unread (the tens of thousands of books that never made it into the canon), claims about culture can better represent the nuanced literary world of the past.

Franco Moretti's Distant Reading.
Franco Moretti’s Distant Reading.

But this is weird. Without exemplars, what the heck are we studying? This isn’t a representation of what’s stood the test of time—that’s the canon we know and love. It’s also not a representation of what was popular back then (well, it sort of was, but more on that shortly), because we don’t know anything about circulation numbers. Most of these Google-scanned books surely never caught the public eye, and many of the now-canonical pieces of literature may not have been popular at the time.

It turns out we kinda suck at figuring out readership statistics, or even at figuring out what was popular at any given time, unless we know what we’re looking for. A folklorist friend of mine has called this the Sophus Bauditz problem. An expert in 19th century Danish culture, my friend one day stumbled across a set of nicely-bound books written by Sophus Bauditz. They were in his era of expertise, but he’d never heard of these books. “Must have been some small print run”, he thought to himself, before doing some research and discovering copies of these books he’d never heard of were everywhere in private collections. They were popular books for the emerging middle class, and sold an order of magnitude more copies than most books of the era; they’d just never made it into the canon. In another century, 50 Shades of Grey will likely suffer the same fate.


In this light, I find The Great Unread to be a weird term.  The Forgotten Read, maybe, to refer to those books which people actually did read but were never canonized, and The Great Tsundoku 4 for those books which were published, lasted to the present, and became digitized, but for which we have no idea whether anyone bothered to read them. The former would likely be more useful in understanding reception, cultural zeitgeist, etc.; the latter might find better use in understanding writing culture and perhaps authorial influence (by seeing whose styles the most other authors copy).

Tsundoku is Japanese for the ever-increasing pile of unread books that have been purchased and added to the queue. Illustrated by Reddit user Wemedge’s 12-year-old daughter.

In the present data-rich world we live in, we can still only grasp at circulation and readership numbers. Library circulation provides some clues, as does the number, size, and sales of print editions. It’s not perfect, of course, though it might be useful in separating zeitgeist from actual readership numbers.

Mathematician Jordan Ellenberg recently coined the tongue-in-cheek Hawking Index, because Stephen Hawking’s books are frequently purchased but rarely read, to measure just that. In his Wall Street Journal article, Ellenberg looked at popular books sold on Amazon Kindle to see where people tended to socially highlight their favorite passages. Highlights from Kahneman’s “Thinking Fast and Slow”, Hawking’s “A Brief History of Time”, and Picketty’s “Capital in the Twenty-First Century” all tended to cluster in the first few pages of the books, suggesting people simply stopped reading once they got a few chapters in.

Kindle and other ebooks certainly complicate matters. It’s been claimed that one reason behind 50 Shades of Grey‘s success was the fact that people could purchase and read it discreetly, digitally, without worry about embarrassment. Digital sales outnumbered print sales for some time into its popularity. As Dan Cohen and Jennifer Howard pointed out, it’s remarkably difficult to understand the ebook market, and the market is quite different among different constituencies. Ebook sales accounted for 23% of the book market this year, yet 50% of romance books are sold digitally.

And let’s not even get into readership statistics for novels that are out copyright, or sold used, or illegally attained: they’re pretty much impossible to count. Consider It’s a Wonderful Life (yes, the 1946 Christmas movie). A clerical accident pushed the movie into the public domain (sort of) in 1974. It had never really been popular before then, but once TV stations could play it without paying royalties, and VHS companies could legally produce and sell copies for free, the movie shot to popularity. Importantly, it shot to popularity in a way that was impossible to see on official license reports, but which Google ngrams reveals quite clearly.

Google ngram count of "It's a Wonderful Life", showing its rise to popularity after the copyright lapse.
Google ngram count of It’s a Wonderful Life, showing its rise to popularity after the 1974 copyright lapse.

This ngram visualization does reveal one good use for The Great Tsundoku, and that’s to use what authors are writing about as finger on the pulse of what people care to write about. This can also be used to track things like linguistic influence. It’s likely no coincidence, for example, that American searches for the word “folks” doubled during the first month’s of President Obama’s bid for the White House in 2007. 5

American searches for the word "folks" during Obama's first presidential bid.
American searches for the word “folks” during Obama’s first presidential bid.

Matthew Jockers has picked up on this capability of The Great Tsundoku for literary history in his analyses of 19th century literature. He compares books by various similar features, and uses that in a discussion of literary influence. Obviously the causal chain is a bit muddled in these cases, culture being ouroboric as it is, and containing a great deal more influencing factors than published books, but it’s a good set of first steps.

But this brings us back to the question of The Great Tsundoku vs. The Forgotten Read, or, what are we learning about when we distant read giant messy corpora like Google Books? This is by no means a novel question. Ted Underwood, Matt Jockers, Ben Schmidt, and I had an ongoing discussion on corpus representativeness a few years back, and it’s been continuously pointed to by corpus linguists 6 and literary historians for some time.

Surely there’s some appreciable difference when analyzing what’s often read versus what’s written?

Surprise! It’s not so simple. Ted Underwood points out:

we could certainly measure “what was printed,” by including one record for every volume in a consortium of libraries like HathiTrust. If we do that, a frequently-reprinted work like Robinson Crusoe will carry about a hundred times more weight than a novel printed only once.

He continues

if we’re troubled by the difference between “what was written” and “what was read,” we can simply create two different collections — one limited to first editions, the other including reprints and duplicate copies. Neither collection is going to be a perfect mirror of print culture. Counting the volumes of a novel preserved in libraries is not the same thing as counting the number of its readers. But comparing these collections should nevertheless tell us whether the issue of popularity makes much difference for a given research question.

While his claim skirts the sorts of issues raised by Ellenberg’s Hawking Index, it does present a very reasonable natural experiment: if you ask the same question of three databases (1. The entire messy, reprint-ridden corpus; 2. Single editions of The Forgotten Read, those books which were popular whether canonized or not; 3. The entire Great Tsundoku, everything that was printed at least once, regardless of whether it was read), what will you find?

Underwood performed 2/3rds of this experiment, comparing The Forgotten Read against the entire HathiTrust corpus on an analysis of the emergence of literary diction. He found that the trend results across both were remarkably similar.

Underwood's analysis of all HathiTrust prose (left), vs. The Forgotten Read (right).
Underwood’s analysis of all HathiTrust prose (47,549 volumes, left), vs. The Forgotten Read (773 volumes, right).

Clearly they’re not precisely the same, but the fact that their trends are so similar is suggestive that the HathiTrust corpus at least shares some traits with The Forgotten Read. The jury is out on the extent of those shared traits, or whether it shares as much with The Great Tsundoku.

The cause of the similarities between historically popular books and books that made it into HathiTrust should be apparent: 7 historically popular books were more frequently reprinted and thus, eventually, more editions made it into the HathiTrust corpus. Also, as Allen Riddell showed, it’s likely that fewer than 60% of published prose from that period have been scanned, and novels with multiple editions are more likely to appear in the HathiTrust corpus.

This wasn’t actually what I was expecting. I figured the HathiTrust corpus would track more closely to what’s written than to what’s read—and we need more experiments to confirm that’s not the case. But as it stands now, we may actually expect these corpora to reflect The Forgotten Read, a continuously evolving measurement of readership and popularity. 8

Lastly, we can’t assume that greater popularity results in larger print runs in every case, or that those larger print runs would be preserved. Ephemera such as zines and comics, digital works produced in the 1980s, and brittle books printed on acidic paper in the 19th century all have their own increased likelihoods of vanishing. So too does work written by minorities, by the subjected, by the conquered.

The Great Unreads

There are, then, quite a few Great Unreads. The Great Tsundoku was coined with tongue planted firmly in-cheek, but we do need a way of talking about the many varieties of Great Unreads, which include but aren’t limited to:

  • Everything ever written or published, along with size of print run, number of editions, etc. (Presumably Moretti’s The Great Unread.)
  • The set of writings which by historical accident ended up digitized.
  • The set of writings which by historical accident ended up digitized, cleaned up with duplicates removed, multiple editions connected and encoded, etc. (The Great Tsundoku.)
  • The set of writings which by historical accident ended up digitized, adjusted for disparities in literacy, class, document preservation, etc. (What we might see if history hadn’t stifled so many voices.)
  • The set of things read proportional to what everyone actually read. (The Forgotten Read.)
  • The set of things read proportional to what everyone actually read, adjusted for disparities in literacy, class, etc.
  • The set of writings adjusted proportionally by their influence, such that highly influential writings are over-represented, no matter how often they’re actually read. (This will look different over time; in today’s context this would be closest to The Canon. Historically it might track closer to a Zeitgeist.)
  • The set of writings which attained mass popularity but little readership and, perhaps, little influence. (Ellenberg’s Hawking-Index.)

And these are all confounded by hazy definitions of publication; slowly changing publication culture; geographic, cultural, or other differences which influence what is being written and read; and so forth.

The important point is that reading at scale is not clear-cut. This isn’t a neglected topic, but nor have we laid much groundwork for formal, shared notions of “corpus”, “collection”, “sample”, and so forth in the realm of large-scale cultural analysis. We need to, if we want to get into serious discussions of validity. Valid with respect to what?

This concludes Part 1. Part 2 will get into the finer questions of validity, surrounding syuzhet and similar projects, and will introduce three new terms (Appreciability, Agreement, and Appropriateness) to approach validity in a more humanities-centric fashion.


  1. Coming in a few weeks because we just received our proofs for The Historian’s Macroscope and I need to divert attention there before finishing this.
  2. And anyway I don’t need to explain myself to you, okay? This post begins where it begins. Syuzhet.
  3. The phrase was originally coined by Margaret Cohen.
  4. (see illustration below)
  5. COCA and other corpus tools show the same trend.
  6. Heather Froelich always has good commentary on this matter.
  7. Although I may be reading this as a just-so story, as Matthew Lincoln pointed out.
  8. This is a huge oversimplification. I’m avoiding getting into regional, class, racial, etc. differences, because popularity obviously isn’t universal. We can also argue endlessly about representativeness, e.g. whether the fact that men published more frequently than women should result in a corpus that includes more male-authored works than female-authored, or whether we ought to balance those scales.

Culturomics 2: The Search for More Money

“God willing, we’ll all meet again in Spaceballs 2: The Search for More Money.” -Mel Brooks, Spaceballs, 1987

A long time ago in a galaxy far, far away (2012 CE, Indiana), I wrote a few blog posts explaining that, when writing history, it might be good to talk to historians (1,2,3). They were popular posts for the Irregular, and inspired by Mel Brooks’ recent interest in making Spaceballs 2,  I figured it was time for a sequel of my own. You know, for all the money this blog pulls in. 1


Two teams recently published very similar articles, attempting cultural comparison via a study of historical figures in different-language editions of Wikipedia. The first, by Gloor et al., is for a conference next week in Japan, and frames itself as cultural anthropology through the study of leadership networks. The second, by Eom et al. and just published in PLoS ONE, explores cross-cultural influence through historical figures who span different language editions of Wikipedia.

Before reading the reviews, keep in mind I’m not commenting on method or scientific contribution—just historical soundness. This often doesn’t align with the original authors’ intents, which is fine. My argument isn’t that these pieces fail at their goals (science is, after all, iterative), but that they would be markedly improved by adhering to the same standards of historical rigor as they adhere to in their home disciplines, which they could accomplish easily by collaborating with a historian.

The road goes both ways. If historians don’t want physicists and statisticians bulldozing through history, we ought to be open to collaborating with those who don’t have a firm grasp on modern historiography, but who nevertheless have passion, interest, and complementary skills. If the point is understanding people better, by whatever means relevant, we need to do it together.

Cultural Anthropology

“Cultural Anthropology Through the Lens of Wikipedia – A Comparison of Historical Leadership Networks in the English, Chinese, Japanese and German Wikipedia” by Gloor et al. analyzes “the historical networks of the World’s leaders since the beginning of written history, comparing them in the four different Wikipedias.”

Their method is simple (simple isn’t bad!): take each “people page” in Wikipedia, and create a network of people based on who else is linked within that page. For example, if Wikipedia’s article on Mozart links to Beethoven, a connection is drawn between them. Connections are only drawn between people whose lives overlap; for example, the Mozart (1756-1791) Wikipedia page also links to Chopin (1810-1849), but because they did not live concurrently, no connection is drawn.

Figure 1 from
Figure 1 from Gloor et al

A separate network is created for four different language editions of Wikipedia (English, Chinese, Japanese, German), because biographies in each edition are rarely exact translations, and often different people will be prominent within the same biography across all four languages. PageRank was calculated for all the people in the resulting networks, to get a sense of who the most central figures are according to the Wikipedia link structure.

“Who are the most important people of all times?” the authors ask, to which their data provides them an answer. 2 In China and Japan, they show, only warriors and politicians make the cut, whereas religious leaders, artists, and scientists made more of a mark on Germany and the English-speaking world. Historians and biographers wind up central too, given how often their names appear on the pages of famous contemporaries on whom they wrote.

Diversity is also a marked difference: 80% of the “top 50” people for the English Wikipedia were themselves non-English, whereas only 4% of the top people from the Chinese Wikipedia are not Chinese. The authors conclude that “probing the historical perspective of many different language-specific Wikipedias gives an X-ray view deep into the historical foundations of cultural understanding of different countries.”

Figure 3
Figure 3 from Gloor et al

Small quibbles aside (e.g. their data include the year 0 BC, which doesn’t exist), the big issue here is the ease with which they claim these are the “most important” actors in history, and that these datasets provides an “X-ray” into the language cultures that produced them. This betrays the same naïve assumptions that plague much of culturomics research: that you can uncritically analyze convenient datasets as a proxy for analyzing larger cultural trends.

You can in fact analyze convenient datasets as a proxy for larger cultural trends, you just need some cultural awareness and a critical perspective.

In this case, several layers of assumptions are open for questioning, including:

  • Is the PageRank algorithm a good proxy for historical importance? (The answer turns out to be yes in some situations, but probably not this one.)
  • Is the link structure in Wikipedia a good proxy for historical dependency? (No, although it’s probably a decent proxy for current cultural popularity of historical figures, which would have been a better framing for this article. Better yet, these data can be used to explore the many well-known and unknown biases that pervade Wikipedia.)
  • Can differences across language editions of Wikipedia be explained by any factors besides cultural differences? (Yes. For example, editors of the German-language Wikipedia may be less likely to write a German biography if one already exists in English, given that ≈64% of Germany speaks English.)

These and other questions, unexplored in the article, make it difficult to take at face value that this study can reveal important historical actors or compare cultural norms of importance. Which is a shame, because simple datasets and approaches like this one can produce culturally and scientifically valid results that wind up being incredibly important. And the scholars working on the project are top-notch, it’s just that they don’t have all the necessary domain expertise to explore their data and questions.

Cultural Interactions

The great thing about PLoS is the quality control on its publications: there isn’t much. As long as primary research is presented, the methods are sound, the data are open, and the experiment is well-documented, you’re in.

It’s a great model: all reasonable work by reasonable people is published, and history decides whether an article is worthy of merit. Contrast this against the current model, where (let’s face it) everything gets published eventually anyway, it’s just a question of how many journal submissions and rounds of peer review you’re willing to sit through. Research sits for years waiting to be published, subject to the whims of random reviewers and editors who may hold long grudges, when it could be out there the minute it’s done, open to critique and improvement, and available to anyone to draw inspiration or to learn from someone’s mistakes.

“Interactions of Cultures and Top People of Wikipedia from Ranking of 24 Language Editions” by Eom et al. is a perfect example of this model. Do I consider it a paragon of cultural research? Obviously not, if I’m reviewing it here. Am I happy the authors published it, respectful of their attempt, and willing to use it to push forward our mutual goal of soundly-researched cultural understanding? Absolutely.

Eom et al.’s piece, similar to that of Gloor et al. above, uses links between Wikipedia people pages to rank historical figures and to make cultural comparisons. The article explores 24 different language editions of Wikipedia, and goes one step further, using the data to explore intercultural influence. Importantly, given that this is a journal-length article and not a paper from a conference proceeding like Gloor et al.’s, extra space and thought was clearly put into the cultural biases of Wikipedia across languages. That said, neither of the articles reviewed here include any authors who identify themselves as historians or cultural experts.

This study collected data a bit differently from the last. Instead of a network connecting only those people whose lives overlapped, this network connected all pages within a single-language edition of Wikipedia, based only on links between articles. 3 They then ranked pages using a number of metrics, including but not limited to PageRank, and only then automatically extracted people to find who was the most prominent in each dataset.

In short, every Wikipedia article is linked in a network and ranked, after which all articles are culled except those about people. The authors explain: “On the basis of this data set we analyze spatial, temporal, and gender skewness in Wikipedia by analyzing birth place, birth date, and gender of the top ranked historical figures in Wikipedia.” By birth place, they mean the country currently occupying the location where a historical figure was born, such that Aristophanes, born in Byzantium 2,300 years ago, is considered Turkish for the purpose of this dataset. The authors note this can lead to cultural misattributions ≈3.5% of the time (e.g. Kant is categorized as Russian, having been born in a city now in Russian territory). They do not, however, call attention to the mutability of culture over time.

Table 2 from Eom et al.
Table 2 from Eom et al.

It is unsurprising, though comforting, to note that the fairly different approach to measuring prominence yields many of the same top-10 results as Gloor’s piece: Shakespeare, Napoleon, Bush, Jesus, etc.

Analysis of the dataset resulted in several worthy conclusions:

  • Many of the “top” figures across all language editions hail from Western Europe or the U.S.
  • Language editions bias local heroes (half of top figures in Wikipedia English are from the U.S. and U.K.; half of those in Wikipedia Hindi are from India) and regional heroes (Among Wikipedia Korean, many top figures are Chinese).
  • Top figures are distributed throughout time in a pattern you’d expect given global population growth, excepting periods representing foundations of modern cultures (religions, politics, and so forth).
  • The farther you go back in time, the less likely a top figure from a certain edition of Wikipedia is to have been born in that language’s region. That is, modern prominent figures in Wikipedia English are from the U.S. or the U.K., but the earlier you go, the less likely top figures are born in English-speaking regions. (I’d question this a bit, given cultural movement and mutability, but it’s still a result worth noting).
  • Women are consistently underrepresented in every measure and edition. More recent top people are more likely to be women than those from earlier years.
Figure 4 from Eom et al.
Figure 4 from Eom et al.

The article goes on to describe methods and results for tracking cultural influence, but this blog post is already tediously long, so I’ll leave that section out of this review.

There are many methodological limitations to their approach, but the authors are quick to notice and point them out. They mention that Linnaeus ranks so highly because “he laid the foundations for the modern biological naming scheme so that plenty of articles about animals, insects and plants point to the Wikipedia article about him.” This research was clearly approached with a critical eye toward methodology.

Eom et al. do not fare as well historically as methodologically; opportunities to frame claims more carefully, or to ask different sorts of questions, are overlooked. I mentioned earlier that the research assumes historical cultural consistency, but cultural currents intersect languages and geography at odd angles.

The fact that Wikipedia English draws significantly from other locations the earlier you look should come as no surprise. But, it’s unlikely English Wikipedians are simply looking to more historically diverse subjects; rather, the locus of some cultural current (Christianity, mathematics, political philosophy) has likely moved from one geographic region to another. This should be easy to test with their dataset by looking at geographic clustering and spread in any given year. It’d be nice to see them move in that direction next.

I do appreciate that they tried to validate their method by comparing their “top people” to lists other historians have put together. Unfortunately, the only non-Wikipedia-based comparison they make is to a book written by an astrophysicist and white separatist with no historical training: “To assess the alignment of our ranking with previous work by historians, we compare it with [Michael H.] Hart’s list of the top 100 people who, according to him, most influenced human history.”

Top People

Both articles claim that an algorithm analyzing Wikipedia networks can compare cultures and discover the most important historical actors, though neither define what they mean by “important.” The claim rests on the notion that Wikipedia’s grand scale and scope smooths out enough authorial bias that analyses of Wikipedia can inductively lead to discoveries about Culture and History.

And critically approached, that notion is more plausible than historians might admit. These two reviewed articles, however, don’t bring that critique to the table. 4 In truth, the dataset and analysis lets us look through a remarkably clear mirror into the cultures that created Wikipedia, the heroes they make, and the roots to which they feel most connected.

Usefully for historians, there is likely much overlap between history and the picture Wikipedia paints of it, but the nature of that overlap needs to be understood before we can use Wikipedia to aid our understanding of the past. Without that understanding, boldly inductive claims about History and Culture risk reinforcing the same systemic biases which we’ve slowly been trying to fix. I’m absolutely certain the authors don’t believe that only 5% of history’s most important figures were women, but the framing of the articles do nothing to dispel readers of this notion.

Eom et al. themselves admit “[i]t is very difficult to describe history in an objective way,” which I imagine is a sentiment we can all get behind. They may find an easier path forward in the company of some historians.


  1. net income: -$120/year.
  2. If you’re curious, the 10 most important people in the English-speaking world, in order, are George W. Bush, ol’ Willy Shakespeare, Sidney Lee, Jesus, Charles II, Aristotle, Napoleon, Muhammad, Charlemagne, and Plutarch.
  3. Download their data here.
  4. Actually the Eom et al. article does raise useful critiques, but mentioning them without addressing them doesn’t really help matters.

The moral role of DH in a data-driven world

This is the transcript from my closing keynote address at the 2014 DH Forum in Lawrence, Kansas. It’s the result of my conflicted feelings on the recent Facebook emotional contagion controversy, and despite my earlier tweets, I conclude the study was important and valuable specifically because it was so controversial.

For the non-Digital-Humanities (DH) crowd, a quick glossary. Distant Reading is our new term for reading lots of books at once using computational assistance; Close Reading is the traditional term for reading one thing extremely minutely, exhaustively.

Networked Society

Distant reading is a powerful thing, an important force in the digital humanities. But so is close reading. Over the next 45 minutes, I’ll argue that distant reading occludes as much as it reveals, resulting in significant ethical breaches in our digital world. Network analysis and the humanities offers us a way out, a way to bridge personal stories with the big picture, and to bring a much-needed ethical eye to the modern world.

Today, by zooming in and out, from the distant to the close, I will outline how networks shape our world and our lives, and what we in this room can do to set a path going forward.

Let’s begin locally.

1. Pale Blue Dot

Pale Blue Dot

You are here. That’s a picture of Kansas, from four billion miles away.

In February 1990, after years of campaigning, Carl Sagan convinced NASA to turn the Voyager 1 spacecraft around to take a self-portrait of our home, the Earth. This is the most distant reading of humanity that has ever been produced.

I’d like to begin my keynote with Carl Sagan’s own words, his own distant reading of humanity. I’ll spare you my attempt at the accent:

Consider again that dot. That’s here. That’s home. That’s us. On it everyone you love, everyone you know, everyone you ever heard of, every human being who ever was, lived out their lives. The aggregate of our joy and suffering, thousands of confident religions, ideologies, and economic doctrines, every hunter and forager, every hero and coward, every creator and destroyer of civilization, every king and peasant, every young couple in love, every mother and father, hopeful child, inventor and explorer, every teacher of morals, every corrupt politician, every ‘superstar,’ every ‘supreme leader,’ every saint and sinner in the history of our species lived there – on a mote of dust suspended in a sunbeam.

What a lonely picture Carl Sagan paints. We live and die in isolation, alone in a vast cosmic darkness.

I don’t like this picture. From too great a distance, everything looks the same. Every great work of art, every bomb, every life is reduced to a single point. And our collective human experience loses all definition. If we want to know what makes us, us, we must move a little closer.

2. Black Rock City

Black Rock City

We’ve zoomed into Black Rock City, more popularly known as Burning Man, a city of 70,000 people that exists for only a week in a Nevada desert, before disappearing back into the sand until the following year. Here life is apparent; the empty desert is juxtaposed against a network of camps and cars and avenues, forming a circle with some ritualistic structure at its center.

The success of Burning Man is contingent on collaboration and coordination; on the careful allocation of resources like water to keep its inhabitants safe; on the explicit planning of organizers to keep the city from descending into chaos year after year.

And the creation of order from chaos, the apparent reversal of entropy, is an essential feature of life. Organisms and societies function through the careful coordination and balance of their constituent parts. As these parts interact, patterns and behaviors emerge which take on a life of their own.

3. Complex Systems

Thus cells combine to form organs, organs to form animals, and animals to form flocks.

We call these networks of interactions complex systems, and we study complex systems using network analysis. Network analysis as a methodology takes as a given that nothing can be properly understood in total isolation. Carl Sagan’s pale blue dot, though poignant and beautiful, is too lonely and too distant to reveal anything of we creatures who inhabit it.

We are not alone.

4. Connecting the Dots

When looking outward rather than inward, we find we are surrounded on all sides by a hundred billion galaxies each with a hundred billion stars. And for as long as we can remember, when we’ve stared up into the night sky, we’ve connected the dots. We’ve drawn networks in the stars in order to make them feel more like us, more familiar, more comprehensible.

Nothing exists in isolation. We use networks to make sense of our place in the vast complex system that contains protons and trees and countries and galaxies.The beauty of network analysis is its ability to transcend differences in scale, such that there is a place for you and for me, and our pieces interact with other pieces to construct the society we occupy. Networks allow us to see the forest and the trees, to give definition to the microcosms and macrocosms which describe the world around us.

5. Networked World

Networks open up the world. Over the past four hundred years, the reach of the West extended to the globe, overtaking trade routes created first by eastern conquerors. From these explorations, we produced new medicines and technologies. Concomitant with this expansion came unfathomable genocide and a slave trade that spanned many continents and far too many centuries.

Despite the efforts of the Western World, it could only keep the effects of globalization to itself for so long. Roads can be traversed in either direction, and the network created by Western explorers, businesses, slave traders, and militaries eventually undermined or superseded the Western centers of power. In short order, the African slave trade in the Americas led to a rich exchange of knowledge of plants and medicines between Native Americans and Africans.

In Southern and Southeast Asia, trade routes set up by the Dutch East India Company unintentionally helped bolster economies and trade routes within Asia. Captains with the company, seeking extra profits, would illicitly trade goods between Asian cities. This created more tightly-knit internal cultural and economic networks than had existed before, and contributed to a global economy well beyond the reach of the Dutch East India Company.

In the 1960s, the U.S. military began funding what would later become the Internet, a global communication network which could transfer messages at unfathomable speeds. The infrastructure provided by this network would eventually become a tool for control and surveillance by governments around the world, as well as a distribution mechanism for fuel that could topple governments in the Middle East or spread state secrets in the United States. The very pervasiveness which makes the internet particularly effective in government surveillance is also what makes it especially dangerous to governments through sites like WikiLeaks.

In short, science and technology lay the groundwork for our networked world, and these networks can be great instruments of creation, or terrible conduits of destruction.

6. Macro Scale

So here we are, occupying this tiny mote of dust suspended in a sunbeam. In the grand scheme of things, how does any of this really matter? When we see ourselves from so great a distance, it’s as difficult to be enthralled by the Sistine Chapel as it is to be disgusted by the havoc we wreak upon our neighbors.

7. Meso Scale

But networks let us zoom in, they let us keep the global system in mind while examining the parts. Here, once again, we see Kansas, quite a bit closer than before. We see how we are situated in a national and international set of interconnections. These connections come in every form, from physical transportation to electronic communication. From this scale, wars and national borders are visible. Over time, cultural migration patterns and economic exchange become apparent. This scale shows us the networks which surround and are constructed by us.


And this is the scale which is seen by the NSA and the CIA, by Facebook and Google, by social scientists and internet engineers. Close enough to provide meaningful aggregations, but far enough that individual lives remain private and difficult to discern. This scale teaches us how epidemics spread, how minorities interact, how likely some city might be a target for the next big terrorist attack.

From here, though, it’s impossible to see the hundred hundred towns whose factories have closed down, leaving many unable to feed their families. It’s difficult to see the small but endless inequalities that leave women and minorities systematically underappreciated and exploited.

8. Micro Scale


We can zoom in further still, Lawrence Kansas at a few hundred feet, and if we watch closely we can spot traffic patterns, couples holding hands, how the seasons affect people’s activities. This scale is better at betraying the features of communities, rather than societies.

But for tech companies, governments, and media distributors, it’s all-too-easy to miss the trees for the forest. When they look at the networks of our lives, they do so in aggregate. Indeed, privacy standards dictate that the individual be suppressed in favor of the community, of the statistical average that can deliver the right sort of advertisement to the right sort of customer, without ever learning the personal details of that customer.

This strange mix of individual personalization and impersonal aggregation drives quite a bit of the modern world. Carefully micro-targeted campaigning is credited with President Barack Obama’s recent presidential victories, driven by a hundred data scientists in an office in Chicago in lieu of thousands of door-to-door canvassers. Three hundred million individually crafted advertisements without ever having to look a voter in the face.

9. Target

And this mix of impersonal and individual is how Target makes its way into the wombs of its shoppers. We saw this play out a few years ago when a furious father went to complain to a Target store manager. Why, he asked the manager, is my high school daughter getting ads for maternity products in the mail? After returning home, the father spoke to his daughter to discover she was, indeed pregnant.  How did this happen? How’d Target know?

 It turns out, Target uses credit cards, phone numbers, and e-mail addresses to give every customer a unique ID. Target discovered a list of about 25 products that, if purchased in a certain sequence by a single customer, is pretty indicative of a customer’s pregnancy. What’s more, the date of the purchased products can pretty accurately predict the date the baby would be delivered. Unscented lotion, magnesium, cotton balls, and washcloths are all on that list.

When Target’s systems learns one of its customers is probably pregnant, it does its best to profit from that pregnancy, sending appropriately timed coupons for diapers and bottles. This backfired, creeping out customers and invading their privacy, as with the angry father who didn’t know his daughter was pregnant. To remedy the situation, rather than ending the personalized advertising, Target began interspersing ads for unrelated products with personalized products in order to trick the customer into thinking the ads were random or general. All the while, a good portion of the coupons in the book were still targeted directly towards those customers.

One Target executive told a New York Times reporter:

We found out that as long as a pregnant woman thinks she hasn’t been spied on, she’ll use the coupons. She just assumes that everyone else on her block got the same mailer for diapers and cribs. As long as we don’t spook her, it works.

The scheme did work, raising Target’s profits by billions of dollars by subtly matching their customers with coupons they were likely to use. 

10. Presidential Elections

Political campaigns have also enjoyed the successes of microtargeting. President Bush’s 2004 campaign pioneered this technique, targeting socially conservative Democratic voters in key states in order to either convince them not to vote, or to push them over the line to vote Republican. This strategy is credited with increasing the pro-Bush African American vote in Ohio from 9% in 2000 to 16% in 2004, appealing to anti-gay marriage sentiments and other conservative values.

The strategy is also celebrated for President Obama’s 2008 and especially 2012 campaigns, where his staff maintained a connected and thorough database of a large portion of American voters. They knew, for instance, that people who drink Dr. Pepper, watch the Golf Channel, drive a Land Rover, and eat at Cracker Barrel are both very likely to vote, and very unlikely to vote Democratic. These insights lead to the right political ads targeted exactly at those they were most likely to sway.

So what do these examples have to do with networks? These examples utilize, after all, the same sorts of statistical tools that have always been available to us, only with a bit more data and power to target individuals thrown in the mix.

It turns out that networks are the next logical step in the process of micronudging, the mass targeting of individuals based on their personal lives in order to influence them toward some specific action.

In 2010, a Facebook study, piggy-backing on social networks, influenced about 340,000 additional people to vote in the US mid-term elections. A team of social scientists at UCSD experimented on 61 million facebook users in order to test the influence of social networks on political action.

A portion of American Facebook users who logged in on election day were given the ability to press an “I voted” button, which shared the fact that they voted with their friends. Facebook then presented users with pictures of their friends who voted, and it turned out that these messages increased voter turnout by about 0.4%. Further, those who saw that close friends had voted were more likely to go out and vote than those who had seen that distant friends voted. The study was framed as “voting contagion” – how well does the action of voting spread among close friends?

This large increase in voter turnout was prompted by a single message on Facebook spread among a relatively small subset of its users. Imagine that, instead of a research question, the study was driven by a particular political campaign. Or, instead, imagine that Facebook itself had some political agenda – it’s not too absurd a notion to imagine.

11. Blackout


In fact, on January 18, 2012, a great portion of the social web rallied under a single political agenda. An internet blackout. In protest of two proposed U.S. congressional laws that threatened freedom of speech on the Web, SOPA and PIPA, 115,000 websites voluntarily blacked out their homepages, replacing them with pleas to petition congress to stop the a bills.

Reddit, Wikipedia, Google, Mozilla, Twitter, Flickr, and others asked their users to petition Congress, and it worked. Over 3 million people emailed their congressional representatives directly, another million sent a pre-written message to Congress from the Electronic Frontier Foundation, a Google petition reached 4.5 million signatures, and lawmakers ultimated collected the names of over 14 million people who protested the bills. Unsurprisingly, the bills were never put up to vote.

These techniques are increasingly being leveraged to influence consumers and voters into acting in-line with whatever campaign is at hand. Social networks and the social web, especially, are becoming tools for advertisers and politicians.

12a. Facebook and Social Guessing

In 2010, Tim Tangherlini invited a few dozen computer scientists, social scientists, and humanists to a two-week intensive NEH-funded summer workshop on network analysis for the humanities. Math camp for nerds, we called it. The environment was electric with potential projects and collaborations, and I’d argue it was this workshop that really brought network analysis to the humanities in force.

During the course of the workshop, one speaker sticks out in my memory: a data scientist at Facebook. He reached the podium, like so many did during those two weeks, and described the amazing feats they were able to perform using basic linguistic and network analyses. We can accurately predict your gender and race, he claimed, regardless of whether you’ve told us. We can learn your political leanings, your sexuality, your favorite band.

Much like most talks from computer scientists at the event, the purpose was to show off the power of large-scale network analysis when applied to people, and didn’t focus much on its application. The speaker did note, however, that they used these measurements to effectively advertise to their users; electronics vendors could advertise to wealthy 20-somethings; politicians could target impoverished African Americans in key swing states.

It was a few throw-away lines in the presentation, but the force of the ensuing questions revolved around those specifically. How can you do this without any sort of IRB oversight? What about the ethics of all this? The Facebook scientist’s responses were telling: we’re not doing research, we’re just running a business.

And of course, Facebook isn’t the only business doing this. The Twitter analytics dashboard allows you to see your male-to-female follower ratio, even though users are never asked their gender. Gender is guessed based on features of language and interactions, and they claim around 90% accuracy.

Google, when it targets ads towards you as a user, makes some predictions based on your search activity. Google guessed, without my telling it, that I am a 25-34 year old male who speaks English and is interested in, among other things, Air Travel, Physics, Comics, Outdoors, and Books. Pretty spot-on.

12b. Facebook and Emotional Contagion

And, as we saw with the Facebook voting study, social web services are not merely capable of learning about you; they are capable of influencing your actions. Recently, this ethical question has pushed its way into the public eye in the form of another Facebook study, this one about “emotional contagion.”

A team of researchers and Facebook data scientists collaborated to learn the extent to which emotions spread through a social network. They selectively filtered the messages seen by about 700,000 Facebook users, making sure that some users only saw emotionally positive posts by their friends, and others only saw emotionally negative posts. After some time passed, they showed that users who were presented with positive posts tended to post positive updates, and those presented with negative posts tended to post negative updates.

The study stirred up quite the controversy, and for a number of reasons. I’ll unpack a few of them:

First of all, there were worries about the ethics of consent. How could Facebook do an emotional study of 700,000 users without getting their consent, first? The EULA that everyone clicks through when signing up for Facebook only has one line saying that data may be used for research purposes, and even that line didn’t appear until several months after the study occurred.

A related issue raised was one of IRB approval: how could the editors at PNAS have approved the study given that the study took place under Facebook’s watch, without an external Institutional Review Board? Indeed, the university-affiliated researchers did not need to get approval, because the data were gathered before they ever touched the study. The counter-argument was that, well, Facebook conducts these sorts of studies all the time for the purposes of testing advertisements or interface changes, as does every other company, so what’s the problem?

A third issue discussed was one of repercussions: if the study showed that Facebook could genuinely influence people’s emotions, did anyone in the study physically harm themselves as a result of being shown a primarily negative newsfeed? Should Facebook be allowed to wield this kind of influence? Should they be required to disclose such information to their users?

The controversy spread far and wide, though I believe for the wrong reasons, which I’ll explain shortly. Social commentators decried the lack of consent, arguing that PNAS shouldn’t have published the paper without proper IRB approval. On the other side, social scientists argued the Facebook backlash was antiscience and would cause more harm than good. Both sides made valid points.

One well-known social scientist noted that the Age of Exploration, when scientists finally started exploring the further reaches of the Americas and Africa, was attacked by poets and philosophers and intellectuals as being dangerous and unethical. But, he argued, did not that exploration bring us new wonders? Miracle medicines and great insights about the world and our place in it?

I call bullshit. You’d be hard-pressed to find a period more rife with slavery and genocide and other horrible breaches of human decency than that Age of Exploration. We can’t sacrifice human decency in the name of progress. On the flip-side, though, we can’t sacrifice progress for the tiniest fears of misconduct. We must proceed with due diligence to ethics without being crippled by inefficacy.

But this is all a red herring. The issue here isn’t whether and to what extent these activities are ethical science, but to what extent they are ethical period, and if they aren’t, what we should do about it. We can’t have one set of ethical standards for researchers, and another for businesses, but that’s what many of the arguments in recent months have boiled down to. Essentially, it was argued, Facebook does this all the time. It’s something called A/B testing: they make changes for some users and not others, and depending on how the users react, they change the site accordingly. It’s standard practice in web development.

13. An FDA/FTC for Data?

It is surprising, then, that the crux of the anger revolved around the published research. Not that Facebook shouldn’t do A/B testing, but that researchers shouldn’t be allowed to publish on it. This seems to be the exact opposite of what should be happening: if indeed every major web company practices these methods already, then scholarly research on how such practices can sway emotions or voting practices are exactly what we need. We must bring these practices to light, in ways the public can understand, and decide as a society whether they cross ethical boundaries. A similar discussion occurred during the early decades of the 20th century, when the FDA and FTC were formed, in part, to prevent false advertising of snake oils and foods and other products.

We are at the cusp of a new era. The mix of big data, social networks, media companies, content creators, government surveillance, corporate advertising, and ubiquitous computing is a perfect storm for intense influence both subtle and far-reaching. Algorithmic nudging has the power to sell products, win elections, topple governments, and oppress a people, depending on how it is wielded and by whom. We have seen this work from the bottom-up, in Occupy Wallstreet, the Revolutions in the Middle East, and the ALS Ice-Bucket Challenge, and from the top-down in recent presidential campaigns, Facebook studies, and coordinated efforts to preserve net neutrality. And these have been works of non-experts: people new to this technology, scrambling in the dark to develop the methods as they are deployed. As we begin to learn more about network-based control and influence, these examples will multiply in number and audacity.

14. Surveillance

And this story leaves out one of the most major players of all: government. When Edward Snowden leaked the details of classified NSA surveillance program, the world was shocked at the government’s interest in and capacity for omniscience. Data scientists, on the other hand, were mostly surprised that people didn’t realize this was happening. If the technology is there, you can bet it will be used.

And so here, in the NSA’s $1.5 billion dollar data center in Utah, are the private phone calls, parking receipts, emails, and Google searches of millions of American citizens. It stores a few exabytes of our data, over a billion gigabytes and roughly equivalent to a hundred thousand times the size of the library of congress. More than enough space, really.

The humanities have played some role in this complex machine. During the Cold War, the U.S. government covertly supported artists and authors to create cultural works which would spread American influence abroad and improve American sentiment at home.

Today the landscape looks a bit different. For the last few years DARPA, the research branch of the U.S. Department of Defense, has been funding research and hosting conferences in what they call “Narrative Networks.” Computer scientists, statisticians, linguists, folklorists, and literary scholars have come together to discuss how ideas spread and, possibly, how to inject certain sentiments within specific communities. It’s a bit like the science of memes, or of propaganda.

Beyond this initiative, DARPA funds have gone toward several humanities-supported projects to develop actionable plans for the U.S. military. One project, for example, creates as-complete-as-possible simulations of cultures overseas, which can model how groups might react to the dropping of bombs or the spread of propaganda. These models can be used to aid in the decision-making processes of officers making life-and-death decisions on behalf of troops, enemies, and foreign citizens. Unsurprisingly, these initiatives, as well as NSA surveillance at home, all rely heavily on network analysis.

In fact, when the news broke on the captures of Osama bin Laden and Saddam Hussein, and how they were discovered via network analysis, some of my family called me after reading the newspapers claiming “we finally understand what you do!” This wasn’t the reaction I was hoping for.

In short, the world is changing incredibly rapidly, in large part driven by the availability of data, network science and statistics, and the ever-increasing role of technology in our lives. Are these corporate, political, and grassroots efforts overstepping their bounds? We honestly don’t know. We are only beginning to have sustained, public discussions about the new role of technology in society, and the public rarely has enough access to information to make informed decisions. Meanwhile, media and web companies may be forgiven for overstepping ethical boundaries, as our culture hasn’t quite gotten around to drawing those boundaries yet.

15. The Humanities’ Place

This is where the humanities come in – not because we have some monopoly on ethics (goodness knows the way we treat our adjuncts is proof we do not) – but because we are uniquely suited to the small scale. To close reading. While what often sets the digital humanities apart from its analog counterpart is the distant reading, the macroanalysis, what sets us all apart is our unwillingness to stray too far from the source. We intersperse the distant with the close, attempting to reintroduce the individual into the aggregate.

Network analysis, not coincidentally, is particularly suited to this endeavor. While recent efforts in sociophysics have stressed the importance of the grand scale, let us not forget that network theory was built on the tiniest of pieces in psychology and sociology, used as a tool to explore individuals and their personal relationships. In the intervening years, all manner of methods have been created to bridge macro and micro, from Granovetter’s theory of weak ties to Milgram’s of Small Worlds, and the way in which people navigate the networks they find themselves in. Networks work at every scale, situating the macro against the meso against the micro.

But we find ourselves in a world that does not adequately utilize this feature of networks, and is increasingly making decisions based on convenience and money and politics and power without taking the human factor into consideration. And it’s not particularly surprising: it’s easy, in the world of exabytes of data, to lose the trees for the forest.

This is not a humanities problem. It is not a network scientist problem. It is not a question of the ethics of research, but of the ethics of everyday life. Everyone is a network scientist. From Twitter users to newscasters, the boundary between people who consume and people who are aware of and influence the global social network is blurring, and we need to deal with that. We must collaborate with industries, governments, and publics to become ethical stewards of this networked world we find ourselves in.

16. Big and Small

Your challenge, as researchers on the forefront of network analysis and the humanities, is to tie the very distant to the very close. To do the research and outreach that is needed to make companies, governments, and the public aware of how perturbations of the great mobile that is our society affect each individual piece.

We have a number of routes available to us, in this respect. The first is in basic research: the sort that got those Facebook study authors in such hot water. We need to learn and communicate the ways in which pervasive surveillance and algorithmic influence can affect people’s lives and steer societies.

A second path towards influencing an international discussion is in the development of new methods that highlight the place of the individual in the larger network. We seem to have a critical mass of humanists collaborating with or becoming computer scientists, and this presents a perfect opportunity to create algorithms which highlight a node’s uniqueness, rather than its similarity.

Another step to take is one of public engagement that extends beyond the academy, and takes place online, in newspapers or essays, in interviews, in the creation of tools or museum exhibits. The MIT Media Lab, for example, created a tool after the Snowden leaks that allows users to download their email metadata to reveal the networks they form. The tool was a fantastic example of a way to show the public exactly what “simply metadata” can reveal about a person, and its viral spread was a testament to its effectiveness. Mike Widner of Stanford called for exactly this sort of engagement from digital humanists a few years ago, and it is remarkable how little that call has been heeded.

Pedagogy is a fourth option. While people cry that the humanities are dying, every student in the country will have taken many humanities-oriented courses by the time they graduate. These courses, ostensibly, teach them about what it means to be human in our complex world. Alongside the history, the literature, the art, let’s teach what it means to be part of a global network, constantly contributing to and being affected by its shadow.

With luck, reconnecting the big with the small will hasten a national discussion of the ethical norms of big data and network analysis. This could result in new government regulating agencies, ethical standards for media companies, or changes in ways people interact with and behave on the social web.

17. Going Forward

When you zoom out far enough, everything looks the same. Occupy Wall Street; Ferguson Riots; the ALS Ice Bucket Challenge; the Iranian Revolution. They’re all just grassroots contagion effects across a social network. Rhetorically, presenting everything as a massive network is the same as photographing the earth from four billion miles: beautiful, sobering, and homogenizing. I challenge you to compare network visualizations of Ferguson Tweets with the ALS Ice Bucket Challenge, and see if you can make out any differences. I couldn’t. We need to zoom in to make meaning.

The challenge of network analysis in the humanities is to bring our close reading perspectives to the distant view, so media companies and governments don’t see everyone as just some statistic, some statistical blip floating on this pale blue dot.

I will end as I began, with a quote from Carl Sagan, reflecting on a time gone by but every bit as relevant for the moment we face today:

I know that science and technology are not just cornucopias pouring good deeds out into the world. Scientists not only conceived nuclear weapons; they also took political leaders by the lapels, arguing that their nation — whichever it happened to be — had to have one first. … There’s a reason people are nervous about science and technology. And so the image of the mad scientist haunts our world—from Dr. Faust to Dr. Frankenstein to Dr. Strangelove to the white-coated loonies of Saturday morning children’s television. (All this doesn’t inspire budding scientists.) But there’s no way back. We can’t just conclude that science puts too much power into the hands of morally feeble technologists or corrupt, power-crazed politicians and decide to get rid of it. Advances in medicine and agriculture have saved more lives than have been lost in all the wars in history. Advances in transportation, communication, and entertainment have transformed the world. The sword of science is double-edged. Rather, its awesome power forces on all of us, including politicians, a new responsibility — more attention to the long-term consequences of technology, a global and transgenerational perspective, an incentive to avoid easy appeals to nationalism and chauvinism. Mistakes are becoming too expensive.

Let us take Carl Sagan’s advice to heart. Amidst cries from commentators on the irrelevance of the humanities, it seems there is a large void which we are both well-suited and morally bound to fill. This is the path forward.

Thank you.

Thanks to Nickoal Eichmann and Elijah Meeks for editing & inspiration.

Bridging Token and Type

There’s an oft-spoken and somewhat strawman tale of how the digital humanities is bridging C.P. Snow’s “Two Culture” divide, between the sciences and the humanities. This story is sometimes true (it’s fun putting together Ocean’s Eleven-esque teams comprising every discipline needed to get the job done) and sometimes false (plenty of people on either side still view the other with skepticism), but as a historian of science, I don’t find the divide all that interesting. As Snow’s title suggests, this divide is first and foremost cultural. There’s another overlapping divide, a bit more epistemological, methodological, and ontological, which I’ll explore here. It’s the nomothetic(type)/idiographic(token) divide, and I’ll argue here that not only are its barriers falling, but also that the distinction itself is becoming less relevant.

Nomothetic (Greek for “establishing general laws”-ish) and Idiographic (Greek for “pertaining to the individual thing”-ish) approaches to knowledge have often split the sciences and the humanities. I’ll offload the hard work onto Wikipedia:

Nomothetic is based on what Kant described as a tendency to generalize, and is typical for the natural sciences. It describes the effort to derive laws that explain objective phenomena in general.

Idiographic is based on what Kant described as a tendency to specify, and is typical for the humanities. It describes the effort to understand the meaning of contingent, unique, and often subjective phenomena.

These words are long and annoying to keep retyping, and so in the longstanding humanistic tradition of using new words for words which already exist, henceforth I shall refer to nomothetic as type and idiographic as token. 1 I use these because a lot of my digital humanities readers will be familiar with their use in text mining. If you counted the number of unique words in a text, you’d be be counting the number of types. If you counted the number of total words in a text, you’d be counting the number of tokens, because each token (word) is an individual instance of a type. You can think of a type as the platonic ideal of the word (notice the word typical?), floating out there in the ether, and every time it’s actually used, it’s one specific token of that general type.

The Token/Type Distinction
The Token/Type Distinction

Usually the natural and social sciences look for general principles or causal laws, of which the phenomena they observe are specific instances. A social scientist might note that every time a student buys a $500 textbook, they actively seek a publisher to punch, but when they purchase $20 textbooks, no such punching occurs. This leads to the discovery of a new law linking student violence with textbook prices. It’s worth noting that these laws can and often are nuanced and carefully crafted, with an awareness that they are neither wholly deterministic nor ironclad.

The humanities (or at least history, which I’m more familiar with) are more interested in what happened than in what tends to happen. Without a doubt there are general theories involved, just as in the social sciences there are specific instances, but the intent is most-often to flesh out details and create a particular internally consistent narrative. They look for tokens where the social scientists look for types. Another way to look at it is that the humanist wants to know what makes a thing unique, and the social scientist wants to know what makes a thing comparable.

It’s been noted these are fundamentally different goals. Indeed, how can you in the same research articulate the subjective contingency of an event while simultaneously using it to formulate some general law, applicable in all such cases? Rather than answer that question, it’s worth taking time to survey some recent research.

A recent digital humanities panel at MLA elicited responses by Ted Underwood and Haun Saussy, of which this post is in part itself a response. One of the papers at the panel, by Long and So, explored the extent to which haiku-esque poetry preceded what is commonly considered the beginning of haiku in America by about 20 years. They do this by teaching the computer the form of the haiku, and having it algorithmically explore earlier poetry looking for similarities. Saussy comments on this work:

[…] macroanalysis leads us to reconceive one of our founding distinctions, that between the individual work and the generality to which it belongs, the nation, context, period or movement. We differentiate ourselves from our social-science colleagues in that we are primarily interested in individual cases, not general trends. But given enough data, the individual appears as a correlation among multiple generalities.

One of the significant difficulties faced by digital humanists, and a driving force behind critics like Johanna Drucker, is the fundamental opposition between the traditional humanistic value of stressing subjectivity, uniqueness, and contingency, and the formal computational necessity of filling a database with hard decisions. A database, after all, requires you to make a series of binary choices in well-defined categories: is it or isn’t it an example of haiku? Is the author a man or a woman? Is there an author or isn’t there an author?

Underwood addresses this difficulty in his response:

Though we aspire to subtlety, in practice it’s hard to move from individual instances to groups without constructing something like the sovereign in the frontispiece for Hobbes’ Leviathan – a homogenous collection of instances composing a giant body with clear edges.

But he goes on to suggest that the initial constraint of the digital media may not be as difficult to overcome as it appears. Computers may even offer us a way to move beyond the categories we humanists use, like genre or period.

Aren’t computers all about “binary logic”? If I tell my computer that this poem both is and is not a haiku, won’t it probably start to sputter and emit smoke?

Well, maybe not. And actually I think this is a point that should be obvious but just happens to fall in a cultural blind spot right now. The whole point of quantification is to get beyond binary categories — to grapple with questions of degree that aren’t well-represented as yes-or-no questions. Classification algorithms, for instance, are actually very good at shades of gray; they can express predictions as degrees of probability and assign the same text different degrees of membership in as many overlapping categories as you like.

Here we begin to see how the questions asked of digital humanists (on the one side; computational social scientists are tackling these same problems) are forcing us to reconsider the divide between the general and the specific, as well as the meanings of categories and typologies we have traditionally taken for granted. However, this does not yet cut across the token/type divide: this has gotten us to the macro scale, but it does not address general principles or laws that might govern specific instances. Historical laws are a murky subject, prone to inducing fits of anti-deterministic rage. Complex Systems Science and the lessons we learn from Agent-Based Modeling, I think, offer us a way past that dilemma, but more on that later.

For now, let’s talk about influence. Or diffusion. Or intertextuality. 2 Matthew Jockers has been exploring these concepts, most recently in his book Macroanalysis. The undercurrent of his research (I think I’ve heard him call it his “dangerous idea”) is a thread of almost-determinism. It is the simple idea that an author’s environment influences her writing in profound and easy to measure ways. On its surface it seems fairly innocuous, but it’s tied into a decades-long argument about the role of choice, subjectivity, creativity, contingency, and determinism. One word that people have used to get around the debate is affordances, and it’s as good a word as any to invoke here. What Jockers has found is a set of environmental conditions which afford certain writing styles and subject matters to an author. It’s not that authors are predetermined to write certain things at certain times, but that a series of factors combine to make the conditions ripe for certain writing styles, genres, etc., and not for others. The history of science analog would be the idea that, had Einstein never existed, relativity and quantum physics would still have come about; perhaps not as quickly, and perhaps not from the same person or in the same form, but they were ideas whose time had come. The environment was primed for their eventual existence. 3

An example of shape affording certain actions by constraining possibilities and influencing people. [via]
An example of shape affording certain actions by constraining possibilities and influencing people. [via]
It is here we see the digital humanities battling with the token/type distinction, and finding that distinction less relevant to its self-identification. It is no longer a question of whether one can impose or generalize laws on specific instances, because the axes of interest have changed. More and more, especially under the influence of new macroanalytic methodologies, we find that the specific and the general contextualize and augment each other.

The computational social sciences are converging on a similar shift. Jon Kleinberg likes to compare some old work by Stanley Milgram 4, where he had people draw maps of cities from memory, with digital city reconstruction projects which attempt to bridge the subjective and objective experiences of cities. The result in both cases is an attempt at something new: not quite objective, not quite subjective, and not quite intersubjective. It is a representation of collective individual experiences which in its whole has meaning, but also can be used to contextualize the specific. That these types of observations can often lead to shockingly accurate predictive “laws” isn’t really the point; they’re accidental results of an attempt to understand unique and contingent experiences at a grand scale. 5

Manhattan. Dots represent where people have taken pictures; blue dots are by locals, red by tourists, and yellow unsure. [via Eric Fischer]
Manhattan. Dots represent where people have taken pictures; blue dots are by locals, red by tourists, and yellow are uncertain. [via Eric Fischer]
It is no surprise that the token/type divide is woven into the subjective/objective divide. However, as Daston and Galison have pointed out, objectivity is not an ahistorical category. 6 It has a history, is only positively defined in relation to subjectivity, and neither were particularly useful concepts before the 19th century.

I would argue, as well, that the nomothetic and idiographic divide is one which is outliving its historical usefulness. Work from both the digital humanities and the computational social sciences is converging to a point where the objective and the subjective can peaceably coexist, where contingent experiences can be placed alongside general predictive principles without any cognitive dissonance, under a framework that allows both deterministic and creative elements. It is not that purely nomothetic or purely idiographic research will no longer exist, but that they no longer represent a binary category which can usefully differentiate research agendas. We still have Snow’s primary cultural distinctions, of course, and a bevy of disciplinary differences, but it will be interesting to see where this shift in axes takes us.


  1. I am not the first to do this. Aviezer Tucker (2012) has a great chapter in The Oxford Handbook of Philosophy of Social Science, “Sciences of Historical Tokens and Theoretical Types: History and the Social Sciences” which introduces and historicizes the vocabulary nicely.
  2. Underwood’s post raises these points, as well.
  3. This has sometimes been referred to as environmental possibilism.
  4. Milgram, Stanley. 1976. “Pyschological Maps of Paris.” In Environmental Psychology: People and Their Physical Settings, edited by Proshansky, Ittelson, and Rivlin, 104–124. New York.

    ———. 1982. “Cities as Social Representations.” In Social Representations, edited by R. Farr and S. Moscovici, 289–309.

  5. If you’re interested in more thoughts on this subject specifically, I wrote a bit about it in relation to single-authorship in the humanities here
  6. Daston, Lorraine, and Peter Galison. 2007. Objectivity. New York, NY: Zone Books.

The Myth of Text Analytics and Unobtrusive Measurement

Text analytics are often used in the social sciences as a way of unobtrusively observing people and their interactions. Humanists tend to approach the supporting algorithms with skepticism, and with good reason. This post is about the difficulties of using words or counts as a proxy for some secondary or deeper meaning. Although I offer no solutions here, readers of the blog will know I am hopeful of the promise of these sorts of measurements if used appropriately, and right now, we’re still too close to the cutting edge to know exactly what that means. There are, however, copious examples of text analytics used well in the humanities (most recently, for example, Joanna Guldi’s  publication on the history of walking).

The Confusion

Klout is a web service which ranks your social influence based on your internet activity. I don’t know how Klout’s algorithm works (and I doubt they’d be terribly forthcoming if I asked), but one of the products of that algorithm is a list of topics about which you are influential. For instance, Klout believes me to be quite influential with regards to Money (really? I don’t even have any of that.) and Journalism (uhmm.. no.), somewhat influential in Juggling (spot on.), Pizza (I guess I am from New York…), Scholarship (Sure!), and iPads (I’ve never touched an iPad.), and vaguely influential on the topic of Cars (nope) and Mining (do they mean text mining?).

By Ildar Sagdejev (Specious) (Own work) [GFDL ( or CC-BY-SA-3.0-2.5-2.0-1.0 (], via Wikimedia Commons
My pizza expertise is clear.
Thankfully careers don’t ride on this measurement (we have other metrics for that), but the danger is still fairly clear: the confusion of vocabulary and syntax for semantics and pragmatics. There are clear layers between the written word and its intended meaning, and those layers often depend on context and prior knowledge. Further, regardless of the intended meaning of the author, how her words are interpreted in the larger world can vary wildly. She may talk about money and pizza until she is blue in the face, but if the whole world disagrees with her, that is no measurement of expertise nor influence (even if angry pizza-lovers frequently shout at her about her pizza opinions).

We see very simple examples of this in sentiment  analysis, a way to extract the attitude of the writer toward whatever it was he’s written. An old friend who recently dipped his fingers in sentiment analysis wrote this:

According to his algorithm, that sentence was a positive one. Unless I seriously misunderstand my social cues (which I suppose wouldn’t be too unlikely), I very much doubt the intended positivity of the author. However, most decent algorithms would pick up that this was a tweet from somebody who was positive about Sarah Jessica Parker.

Unobtrusive Measurements

This particular approach to understanding humans belongs to the larger methodological class of unobtrusive measurements. Generally speaking, this topic is discussed in the context of the social sciences and is contrasted with more ‘obtrusive’ measurements along the lines of interviews or sticking people in labs. Historians generally don’t need to talk about unobtrusive measurements because, hey, the only way we could be obtrusive to our subjects would require exhuming bodies. It’s the idea that you can cleverly infer things about people from a distance, without them knowing that they are being studied.

Notice the disconnect between what I just said, and the word itself. ‘Unobtrusive’ against “without them knowing that they are being studied.” These are clearly not the same thing, and that distinction between definition and word is fairly important – and not merely in the context of this discussion. One classic example (Doob and Gross, 1968) asks how somebody’s social status determines whether someone might take aggressive action against them. They specifically measures a driver’s likelihood to honk his horn in frustration based on the perceived social status of the driver in front of them. Using a new luxury car and an old rusty station wagon, the researchers would stop at traffic lights that had turned green and would wait to see whether the car behind them honked. In the end, significantly more people honked at the low status car. More succinctly: status affects decisions of aggression.  Honking and the perceived worth of the car were used as proxies for aggression and perceptions of status, much like vocabulary is used as a proxy for meaning.

In no world would this be considered unobtrusive from the subject’s point of view. The experimenters intruded on their world, and their actions and lives changed because of it. All it says is that the subjects won’t change their behavior based on the knowledge that they are being studied. However, when an unobtrusive experiment becomes large enough, even one as innocuous as counting words, even that advantage no longer holds. Take, for example, citation analysis and the h-index. Citation analysis was initially construed as an unobtrusive measurement; we can say things about scholars and scholarly communication by looking at their citation patterns rather than interviewing them directly. However, now that entire nations (like Australia or the UK) use quantitative analysis to distribute funding to scholarship, the measurements are no longer unobtrusive. Scholars know how the new scholarly economy works, and have no problem changing their practices to get tenure, funding, etc.

The Measurement and The Effect: Untested Proxies

A paper was recently published (O’Boyle Jr. and Aguinis, 2012) on the non-normality of individual performance. The idea is that we assume that people’s performance (for example students in a classroom) are normally distributed along a bell curve. A few kids get really good grades, a few kids get really bad grades, but most are ‘C’ students. The authors challenge this view, suggesting performance takes on more of a power-law distribution, where very few people perform very well, and the majority perform very poorly, with 80% of people performing worse than the statistical average. If that’s hard to imagine, it’s because people are trained to think of averages on a bell curve, where 50% are greater than average and 50% are worse than average. Instead, imagine one person gets a score of 100, and another five people get scores of 10. The average is (100 + (10 * 5)) / 6 = 25, which means five out of the six people performed worse than average.

It’s an interesting hypothesis, and (in my opinion) probably a correct one, but their paper does not do a great job showing that. The reason is (you guessed it) they use scores as a proxy for performance.  For example, they look at the number of published papers individuals have in top-tier journals, and show that some authors are very productive whereas most are not. However, it’s a fairly widely-known phenomena that in science, famous names are more likely to be published than obscure ones (there are many anecdotes about anonymous papers being rejected until the original, famous author is revealed, at which point the paper is magically accepted). The number of accepted papers may be as much a proxy for fame as it is for performance, so the results do not support their hypothesis. The authors then look at awards given to actors and writers, however those awards suffer the same issues: the more well-known an actor, the the more likely they’ll be used in good movies, the more likely they’ll be visible to award-givers, etc. Again, awards are not a proxy for the quality of a performance. The paper then goes on to measure elected officials based on votes in elections. I don’t think I need to go on about how votes might not map one-to-one on the performance and prowess of an elected official.

I blogged a review of the most recent culturomics paper, which used google ngrams to look at the frequency of recurring natural disasters (earthquakes, floods, etc.) vs. the frequency of recurring social events (war, unemployment, etc.). The paper concludes that, because of differences in the frequency of word-use for words like ‘war’ or ‘earthquake’, the phenomena themselves are subject to different laws. The authors use word frequency as a proxy for the frequency of the events themselves, much in the same way that Klout seems to measure influence based on word-usage and counting. The problem, of course, is that the processes which govern what people decide to write down do not enjoy a one-to-one relationship to what people experience. Using words as proxies for events is just as problematic as using them for proxies of expertise, influence, or performance. The underlying processes are simply far more complicated than these algorithms give them credit for.

It should be noted, however, that the counts are not meaningless; they just don’t necessarily work as proxies for what these ngram scholars are trying to measure. Further, although the underlying processes are quite complex, the effect size of social or political pressure on word-use may be negligible to the point that their hypothesis is actually correct. The point isn’t that one cannot use one measurement as a proxy for something else; rather, the effectiveness of that proxy is assumed rather than actually explored or tested in any way. We need to do a better job, especially as humanists, of figuring out exactly how certain measurements map onto effects we seek.

A beautiful case study that exemplifies this point was written by famous statistician Andrew Gelman, and it aims to use unobtrusive and indirect measurements to find alien attacks and zombie outbreaks. He uses Google Trends to show that the number of zombies in the world are growing at a frightening rate.

Zombies will soon take over!


More heavy-handed culturomics

A few days ago, Gao, Hu, Mao, and Perc posted a preprint of their forthcoming article comparing social and natural phenomena. The authors, apparently all engineers and physicists, use the google ngrams data to come to the conclusion that “social and natural phenomena are governed by fundamentally different processes.” The take-home message is that words describing natural phenomena increase in frequency at regular, predictable rates, whereas the use of certain socially-oriented words change in unpredictable ways. Unfortunately, the paper doesn’t necessarily differentiate between words and what they describe.

Specifically, the authors invoke random fractal theory (sort of a descendant of chaos theory) to find regular patterns in 1-grams. A 1-gram is just a single word, and this study looks at how the frequency of certain words grow or shrink over time. A “hurst parameter” is found for 24 words, a dozen pertaining to nature (earthquake, fire, etc.), and another dozen “social” words (war, unemployment, etc.). The hurst parameter (H) is a number which, essentially, reveals whether or not a time series of data is correlated with itself. That is, given a set of observations over the last hundred years, autocorrelated data means the observation for this year will very likely follow a predictable trend from the past.

If H is between 0.5 and 1, that means the dataset has “long-term positive correlation,” which is roughly equivalent to saying that data quite some time in the past will still positively and noticeably effect data today. If H is under 0.5, data are negatively correlated with their past, suggesting that a high value in the past implies a low value in the future, and if H = 0.5, the data likely describe Brownian motion (they are random). H can exceed 1 as well, a point which I’ll get to momentarily.

The authors first looked at the frequency of 12 words describing natural phenomena between 1770 and 2007. In each case, H was between 0.5 and 1, suggesting a long-term positive trend in the use of the terms. That is, the use of the term “earthquake” does not fluctuate terribly wildly from year to year; looking at how frequently it was used in the past can reasonably predict how frequently it will be used in the future. The data have a long “memory.”

Natural 1-grams from Gao et al. (2012)

The paper then analyzed 12 words describing social phenomena, with very different results. According to the authors, “social phenomena, apart from rare exceptions, cannot be classified solely as processes with persistent-long range correlations.” For example, the use of the word “war” bursts around World War I and World War II; these are unpredictable moments in the discussion of social phenomena. The way “war” was used in the past was not a good predictor of how “war” would be used around 1915 and 1940, for obvious reasons.

Social 1-grams from Gao et al. (2012)

You may notice that, for many of the social terms, H is actually greater than 1, “which indicates that social phenomena are most likely to be either nonstationary, on-off intermittent, or Levy walk-like process.” Basically, the H parameter alone is not sufficient to describe what’s going on with the data. Nonstationary processes are, essentially, unpredictable. A stationary process can be random, but at least certain statistical properties of that randomness remain persistent. Nonstationary processes don’t have those persistent statistical properties. The authors point out that not all social phenomena will have H >1, citing famine, because it might relate to natural phenomena. They also point out that “the more the social phenomena can be considered recent (unemployment, recession, democracy), the higher their Hurst parameter is likely to be.”

In sum, they found that “The prevalence of long-term memory in natural phenomena [compels them] to conjecture that the long-range correlations in the usage frequency of the corresponding terms is predominantly driven by occurrences in nature of those phenomena,” whereas “it is clear that all these processes [describing social phenomena] are fundamentally different from those describing natural phenomena.” That the social phenomena follow different laws is not unexpected, they say, because they themselves are more complex; they rely on political, economic, and social forces, as well as natural phenomena.

While this paper is exceptionally interesting, and shows a very clever use of fairly basic data (24 one-dimensional variables, just looking at word use per year), it lacks the same sort of nuance also lacking in the original culturomics paper. Namely, in this case, it lacks the awareness that social and natural phenomena are not directly coupled with the words used to describe them, nor the frequency with which those words are used. The paper suggests that natural and social phenomena are governed by different scaling laws when, realistically, it is the way they are discussed, and how those discussions are published which are governed by the varying scaling laws. Further, although they used words exemplifying the difference between “nature” and “society,” the two are not always so easily disentangled, either in language or the underlying phenomena.

Perhaps the sort of words used to describe social events change differently than the sort used to describe natural events. Perhaps, because natural phenomena are often immediately felt across vast distances, whereas news of social phenomena can take some time to diffuse, how rapidly some words are discussed may take very different forms. Discussions and word-usage are always embedded in a larger network. Also needing to be taken into account is who is discussing social vs. natural phenomena, and which is more likely to get published and preserved to eventually be scanned by Google Books.

Without a doubt the authors have noticed a very interesting trend, but rather than matching the phenomena directly to word, as they did, we should be using this sort of study to look at how language changes, how people change, and ultimately what relationship people have with the things they discuss and publish. At this point, the engineers and physicists still have a greater comfort with the statistical tools needed to fully utilize the google books corpus, but there are some humanists out there already doing absolutely fantastic quantitative work with similar data.

This paper, while impressive, is further proof that the quantitative study of culture should not be left to those with (apparently) little background in the subject. While it is not unlikely that different factors do, in fact, determine the course of natural disasters versus that of human interaction, this paper does not convincingly tease those apart. It may very well be that the language use is indicative of differences in underlying factors in the phenomena described, however no study is cited suggesting this to be the case. Claims like “social and natural phenomena are governed by fundamentally different processes,” given the above language data, could easily have been avoided, I think, with a short discussion between the authors and a humanist.

The Networked Structure of Scientific Growth

Well, it looks like Digital Humanities Now scooped me on posting my own article. As some of you may have read, I recently did not submit a paper on the Republic of Letters, opting instead to hold off until I could submit it to a journal which allowed authorial preprint distribution. Preprints are a vital part of rapid knowledge exchange in our ever-quickening world, and while some disciplines have embraced the preprint culture, many others have yet to. I’d love the humanities to embrace that practice, and in the spirit of being the change you want to see in the world, I’ve decided to post a preprint of my Republic of Letters paper, which I will be submitting to another journal in the near future. You can read the full first draft here.

The paper, briefly, is an attempt to contextualize the Republic of Letters and the Scientific Revolution using modern computational methodologies. It draws from secondary sources on the Republic of Letters itself, especially from my old mentor R.A. Hatch, some network analysis from sociology and statistical physics, modeling, human dynamics, and complexity theory. All of this is combined through datasets graciously donated by the Dutch Circulation of Knowledge group and Oxford’s Cultures of Knowledge project, totaling about 100,000 letters worth of metadata. Because it favors large scale quantitative analysis over an equally important close and qualitative analysis, the paper is a contribution to historiopgraphic methodology rather than historical narrative; that is, it doesn’t say anything particularly novel about history, but it does offer a (fairly) new way of looking at and contextualizing it.

A visualization of the Dutch Republic of Letters using Sci2 & Gephi

At its core, the paper suggests that by looking at how scholarly networks naturally grow and connect, we as historians can have new ways to tease out what was contingent upon the period and situation. It turns out that social networks of a certain topology are basins of attraction similar to those I discussed in Flow and Empty Space. With enough time and any of a variety of facilitating social conditions and technologies, a network similar in shape and influence to the Republic of Letters will almost inevitably form. Armed with this knowledge, we as historians can move back to the microhistories and individuated primary materials to find exactly what those facilitating factors were, who played the key roles in the network, how the network may differ from what was expected, and so forth. Essentially, this method is one base map we can use to navigate and situate historical narrative.

Of course, I make no claims of this being the right way to look at history, or the only quantitative base map we can use. The important point is that it raises new kinds of questions and is one mechanism to facilitate the re-integration of the individual and the longue durée, the close and the distant reading.

The project casts a necessarily wide net. I do not yet, and probably could not ever, have mastery over each and every disciplinary pool I draw from. With that in mind, I welcome comments, suggestions, and criticisms from historians, network analysts, modelers, sociologists, and whomever else cares to weigh in. Whomever helps will get a gracious acknowledgement in the final version, good scholarly karma, and a cookie if we ever meet in person. The draft will be edited and submitted in the coming months, and if you have ideas, please post them in the comment section below. Also, if you use ideas from the paper, please cite it as an unpublished manuscript or, if it gets published, cite that version instead.

Early Modern Letters Online

Early modern history! Science! Letters! Data! Four of my favoritest things have been combined in this brand new beta release of Early Modern Letters Online from Oxford University.



EMLO (what an adorable acronym, I kind of what to tickle it) is Oxford’s answer to a metadata database (metadatabase?) of, you guessed it, early modern letters. This is pretty much a gold standard metadata project. It’s still in beta, so there are some interface kinks and desirable features not-yet-implemented, but it has all the right ingredients for a great project:

  • Information is free and open; I’m even told it will be downloadable at some point.
  • Developed by a combination of historians (via Cultures of Knowledge) and librarians (via the Bodleian Library) working in tandem.
  • The interface is fast, easy, and includes faceted browsing.
  • Has a fantastic interface for adding your own data.
  • Actually includes citation guidelines thank you so much.
  • Visualizations for at-a-glance understanding of data.
  • Links to full transcripts, abstracts, and hard-copies where available.
  • Lots of other fantastic things.

Sorry if I go on about how fantastic this catalog is – like I said, I love letters so much. The index itself includes roughly 12,000 people, 4,000 locations, 60,000 letters, 9,000 images, and 26,000 additional comments. It is without a doubt the largest public letters database currently available. Between the data being compiled by this group, along with that of the CKCC in the Netherlands, the Electronic Enlightenment Project at Oxford, Stanford’s Mapping the Republic of Letters project, and R.A. Hatch‘s research collection, there will without a doubt soon be hundreds of thousands of letters which can be tracked, read, and analyzed with absolute ease. The mind boggles.

Bodleian Card Catalogue Summaries

Without a doubt, the coolest and most unique feature this project brings to the table is the digitization of Bodleian Card Catalogue, a fifty-two drawer index-card cabinet filled with summaries of nearly 50,000 letters held in the library, all compiled by the Bodleian staff many years ago. In lieu of full transcriptions, digitizations, or translations, these summary cards are an amazing resource by themselves. Many of the letters in the EMLO collection include these summaries as full-text abstracts.

One of the Bodleian summaries showing Heinsius looking far and wide for primary sources, much like we’re doing right now…

The collection also includes the correspondences of John Aubrey (1,037 letters), Comenius (526), Hartlib (4,589 many including transcripts), Edward Lhwyd (2,139 many including transcripts), Martin Lister (1,141), John Selden (355), and John Wallis (2,002). The advanced search allows you to look for only letters with full transcripts or abstracts available. As someone who’s worked with a lot of letters catalogs of varying qualities, it is refreshing to see this one being upfront about unknown/uncertain values. It would, however, be nice if they included the editor’s best guess of dates and locations, or perhaps inferred locations/dates from the other information available. (For example, if birth and death dates are known, it is likely a letter was not written by someone before or after those dates.)


In the interest of full disclosure, I should note that, much like with the CKCC letters interface, I spent some time working with the Cultures of Knowledge team on visualizations for EMLO. Their group was absolutely fantastic to work with, with impressive resources and outstanding expertise. The result of the collaboration was the integration of visualizations in metadata summaries, the first of which is a simple bar chart showing the numbers of letters written, received, and mentioned in per year of any given individual in the catalog. Besides being useful for getting an at-a-glance idea of the data, these charts actually proved really useful for data cleaning.

Sir Robert Crane (1604-1643)

In the above screenshot from previous versions of the data, Robert Crane is shown to have been addressed letters in the mid 1650s, several years after his reported death. While these could also have been spotted automatically, there are many instances where a few letters are dated very close to a birth or death date, and they often turn out to miss-reported. Visualizations can be great tools for data cleaning as a form of sanity test. This is the new, corrected version of Robert Crane’s page. They are using d3.js, a fantastic javascript library for building visualizations.

Because I can’t do anything with letters without looking at them as a network, I decided to put together some visualizations using Sci2 and Gephi. In both cases, the Sci2 tool was used for data preparation and analysis, and the final network was visualized in GUESS and Gephi, respectively. The first graph shows network in detail with edges, and names visible for the most “central” correspondents. The second visualization is without edges, with each correspondent clustered according to their place in the overall network, with the most prominent figures in each cluster visible.

Built with Sci2/Guess
Built with Sci2/Gephi

The graphs show us that this is not a fully connected network. There are many islands of one or two letters or a small handful of letters. These can be indicative of a prestige bias in the data. That is, the collection contains many letters from the most prestigious correspondents, and increasingly fewer as the prestige of the correspondent decreases. Put in another way, there are many letters from a few, and few letters from many. This is a characteristic shared with power law and other “long tail” distributions. The jumbled community structure at the center of the second graph is especially interesting, and it would be worth comparing these communities against institutions and informal societies at the time. Knowledge of large-scale patterns in a network can help determine what sort of analyses are best for the data at hand. More on this in particular will be coming in the next few weeks.

It’s also worth pointing out these visualizations as another tool for data-checking. You may notice, on the bottom left-hand corner of the first network visualization, two separate Edward Lhwyds with virtually the same networks of correspondence. This meant there were two distinct entities in their database referring to the same individual – a problem which has since been corrected.

More Letters!

Notice that the EMLO site makes it very clear that they are open to contributions. There are many letters datasets out there, some digitized, some still languishing idly on dead trees, and until they are all combined, we will be limited in the scope of the research possible. We can always use more. If you are in any way responsible for an early-modern letters collection, meta-data or full-text, please help by opening that collection up and making it integrable with the other sets out there. It will do the scholarly world a great service, and get us that much closer to understanding the processes underlying scholarly communication in general. The folks at Oxford are providing a great example, and I look forward to watching this project as it grows and improves.

Quick Followup to Avoiding Traps

So apparently yesterday was a big day for hypothesis testing and discovery. Stanley Fish’s third post on Digital Humanities also brought up the issue of fishing for correlations, although his post was… slightly more polemic. Rather than going over it on this blog, I’ll let Ted Underwood describe it. Anybody who read my post on Avoiding Traps should also read Underwood’s post; it highlights the role of discovery in the humanities as a continuous process of appraisal and re-appraisal, both on the quantitative and qualitative side.

…the significance of any single test is reduced when it’s run as part of a large battery.

That’s a valid observation, 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 (targeted 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 more stringently. (For instance, after noticing that coastal states initially seem more prominent in American fiction than the Midwest, he tests whether this remains true after you compensate for differences in population size, and then proposes a hypothesis that he suggests will need to be confirmed by additional “test cases.”)

It’s important to keep in mind that Reichenbach’s old distinction between discovery and justification is not so clear-cut as it was originally conceived. How we generate our hypotheses, and how we support them to ourselves and the world at large, is part of the ongoing process of research. In my last post, I suggested people keep clear ideas of what they plan on testing before they begin testing; let me qualify that slightly. One of the amazing benefits of Big Data has been the ability to spot trends we were not looking for; an unexpected trend in the data can lead us to a new hypothesis, one which might be fruitful and interesting. The task, then, is to be clever enough to devise further tests to confirm the hypothesis in a way that isn’t circular, relying on the initial evidence that led you toward it.

… I like books with pictures. When I started this blog, I promised myself I’d have a picture in every post. I can’t think of one that’s relevant, so here’s an angry cupcake:

Avoiding traps

We have the advantage of arriving late to the game.

In the cut-throat world of high-tech venture capitalism, the first company with a good idea often finds itself at the mercy of latecomers. The latecomer’s product might be better-thought-out, advertised to a more appropriate market, or simply prettier, but in each case that improvement comes through hindsight. Trailblazers might get there first, but their going is slowest, and their way the most dangerous.

Digital humanities finds itself teetering on the methodological edge of many existing disciplines, boldly going where quite a few have gone before. When I’ve blogged before about the dangers of methodology appropriation, it was in the spirit of guarding against our misunderstanding of foundational aspects of various methodologies. This post is instead about avoiding the monsters already encountered (and occasionally vanquished) by other disciplines.

If a map already exists with all the dragons' hideouts, we should probably use it. (Image from the Carta Marina)

Everything Old Is New Again

A collective guffaw probably accompanied my defining digital humanities as a “new” discipline. Digital humanities itself has a rich history dating back to big iron computers in the 1950s, and the humanities in general, well… they’re old. Probably older than my grandparents.

The important point, however, is that we find ourselves in a state of re-definition. While this is not the first time, and it certainly will not be the last, this state is exceptionally useful in planning against future problems. Our blogosphere cup overfloweth with definitions of and guides to the digital humanities, many of our journals are still in their infancy, and our curricula are over-ready for massive reconstruction. Generally (from what I’ve seen), everyone involved in these processes are really excited and open to new ideas, which should ease the process of avoiding monsters.

Most of the below examples, and possible solutions, are drawn from the same issues of bias I’ve previously discussed. Also, the majority are meta-difficulties. While some of the listed dangers are avoidable when writing papers and doing research, most are discipline-level systematic. That is, despite any researcher’s best efforts, the aggregate knowledge we gain while reading the newest exciting articles might fundamentally mislead us. While these dangers have never been wholly absent from the humanities, our recent love of big data profoundly increases their effect sizes.

An architect from Florida might not be great at designing earthquake-proof housing, and while earthquakes are still a distant danger, this shouldn’t really affect how he does his job at home. If the same architect moves to California, odds are he’ll need to learn some extra precautions. The same is true for a digital humanist attempting to make inferences from lots of data, or from a bunch of studies which all utilize lots of data. Traditionally, when looking at the concrete and particular, evidence for something is necessary and (with enough evidence) sufficient to believe in that thing. In aggregate, evidence for is necessary but not sufficient to identify a trend, because that trend may be dwarfed by or correlated to some other data that are not available.

Don't let Florida architects design your California home. (Image by Claudio Núñez, through Wikimedia Commons)

The below lessons are not all applicable to DH as it exists today, and of course we need to adapt them to our own research (their meaning changes in light of our different material of study), however they’re still worth pointing out and, perhaps, may be guarded against. Many traditional sciences still struggle with these issues due to institutional inertia. Their journals have acted in such a way for so long, so why change it now? Their tenure has acted in such a way for so long, so why change it now? We’re already restructuring, and we have a great many rules that are still in flux, so we can change it now.

Anyway, I’ve been dancing around the examples for way too long, so here’s the meat:

Sampling and Selection Bias

The problem here is actually two-fold, both for the author of a study, and for the reader of several studies. We’ll start with the author-centric issues.

Sampling and Selection Bias in Experimental Design

People talk about sampling and selection biases in different ways, but for the purpose of this post we’ll use wikipedia’s definition:

Selection bias is a statistical bias in which there is an error in choosing the individuals or groups to take part in a scientific study.

A distinction, albeit not universally accepted, of sampling bias [from selection bias] is that it undermines the external validity of a test (the ability of its results to be generalized to the rest of the population), while selection bias mainly addresses internal validity for differences or similarities found in the sample at hand. In this sense, errors occurring in the process of gathering the sample or cohort cause sampling bias, while errors in any process thereafter cause selection bias.

In this case, we’ll say a study exhibits a sampling error if the conclusions drawn from the data at hand, while internally valid, does not actually hold true for the world around it. Let’s say I’m analyzing the prevalence of certain grievances in the cahiers de doléances from the French Revolution. One study showed that, of all the lists written, those from urban areas were significantly more likely to survive to today. Any content analysis I perform on those lists will bias the grievances of those people from urban areas, because my sample is not representative. Conclusions I draw about grievances in general will be inaccurate, unless I explicitly take into account which sort of documents I’m missing.

Selection bias can be insidious, and many varieties can be harder to spot than sampling bias. I’ll discuss two related phenomena of selection bias which lead to false positives, those pesky statistical effects which leave us believing we’ve found something exciting when all we really have is hot air.

Data Dredging

The first issue, probably the most relevant to big-data digital humanists, is data dredging. When you have a lot of data (and increasingly more of us have just that), it’s very tempting to just try to find correlations between absolutely everything. In fact, as exploratory humanists, that’s what we often do: get a lot of stuff, try to understand it by looking at it from every angle, and then write anything interesting we notice. This is a problem. The more data you have, the more statistically likely it is that it will contain false-positive correlations.

Google has lots of data, let’s use them as an example! We can look at search frequencies over time to try to learn something about the world. For example, people search for “Christmas” around and leading up to December, but that search term declines sharply once January hits. Comparing that search with searches for “Santa”, we see the two results are pretty well correlated, with both spiking around the same time. From that, we might infer that the two are somehow related, and would do some further studies.

Unfortunately, Google has a lot of data, and a lot of searches, and if we just looked for every search term that correlated well with any other over time, well, we’d come up with a lot of nonsense. Apparently searches for “losing weight” and “2 bedroom” are 93.6% correlated over time. Perhaps there is a good reason, perhaps there is not, but this is a good cautionary tale that the more data you have, the more seemingly nonsensical correlations will appear. It is then very easy to cherry pick only the ones that seem interesting to you, or which support your hypothesis, and to publish those.

Comparing searches for "losing weight" (blue) against "2 bedroom" (red) over time, using Google Trends.

Cherry Picking

The other type of selection bias leading to false positives I’d like to discuss is cherry picking. This is selective use of evidence, cutting data away until the desired hypothesis appears to be the correct one. The humanities, not really known for their hypothesis testing, are not quite as likely to be bothered by this issue, but it’s still something to watch out for. This is also related to confirmation bias, the tendency for people to only notice evidence for that which they already believe.

Much like data dredging, cherry picking is often done without the knowledge or intent of the research. It arises out of what Simmons, Nelson, and Simonsohn (2011) call researcher degrees of freedom. Researchers often make decisions on the fly:

Should more data be collected? Should some observations be excluded? Which conditions should be combined and which ones compared? Which control variables should be considered? Should specific measures be combined or transformed or both?

The problem, of course, is that the likelihood of at least one (of many) analyses producing a falsely positive finding [that is significant] is [itself necessarily significant]. This exploratory behavior is not the by-product of malicious intent, but rather the result of two factors: (a) ambiguity in how best to make these decisions and (b) the researcher’s desire to find a statistically significant result.

When faced with decisions of how to proceed with analysis, we will almost invariably (and inadvertently) favor the decision that results in our hypothesis seeming more plausible.

If I go into my favorite dataset (The Republic of Letters!) trying to show that Scholar A was very similar to Scholar B in many ways, odds are I could do that no matter who the scholars were, so long as I had enough data. If you take a cookie-cutter to your data, don’t be surprised when cookie-shaped bits come out the other side.

Sampling and Selection Bias in Meta-Analysis

There are copious examples of problems with meta-analysis. Meta-analysis is, essentially, a quantitative review of studies on a particular subject. For example, a medical meta-analysis could review data from hundreds of small studies testing the side-effects of a particular medicine, bringing them all together and drawing new or more certain conclusions via the combination of data. Sometimes these are done to gain a larger sample size, or to show how effects change across different samples, or to provide evidence that one non-conforming study was indeed a statistical anomaly.

A meta-analysis is the quantitative alternative to something every one of us in academia does frequently: read a lot of papers or books, find connections, draw inferences, explore new avenues, and publish novel conclusions. Because quantitative meta-analysis is so similar to what we do, we can use the problems it faces to learn more about the problems we face, but which are more difficult to see. A criticism oft-lobbed at meta-analyses is that of garbage in – garbage out; the data used for the meta-analysis is not representative (or otherwise flawed), so the conclusions as well are flawed.

There are a number of reasons why the data in might be garbage, some of which I’ll cover below. It’s worth pointing out that the issues above (cherry-picking and data dredging) also play a role, because if the majority of studies are biased toward larger effect sizes, then the overall perceived effect across papers will appear systematically larger. This is not only true of quantitative meta-analysis; when every day we read about trends and connections that may not be there, no matter how discerning we are, some of those connections will stick and our impressions of the world will be affected. Correlation might not imply anything.

Before we get into publication bias,  I will write a short aside that I was really hoping to avoid, but really needs to be discussed. I’ll dedicate a post to it eventually, when I feel like punishing myself, but for now, here’s my summary of

The Problems with P

Most of you have heard of p-values. A lucky few of you have never heard of them, and so do not need to be untrained and retrained. A majority of you probably hold a view similar to a high-ranking, well-published, and well-learned professor I met recently. “All I know about statistics,” he said, “is that p-value formula you need to show whether or not your hypothesis is correct. It needs to be under .05.” Many of you (more and more these days) are aware of the problems with that statement, and I thank you from the bottom of my heart.

Let’s talk about statistics.

The problems with p-values are innumerable (let me count the ways), and I will not get into most of them here. Essentially, though, the calculation of a p-value is the likelihood that the results of your study did not appear by random chance alone. In many studies which rely on statistics, the process works like this: begin with a hypothesis, run an experiment, analyze the data, calculate the p-value. The researcher then publishes something along the lines of “my hypothesis is correct because p is under 0.05.”

Most people working with p-values know that it has something to do with the null hypothesis (that is, the default position; the position that there is no correlation between the measured phenomena). They work under the assumption that the p-value is the likelihood that the null hypothesis is true. That is, if the p-value is 0.75, it’s 75% likely that the null hypothesis is true, and there is no correlation between the variables being studied. Generally, the cut-off to get published is 0.05; you can only publish your results if it’s less than 5% likely that the null hypothesis is true, or more than 95% likely that your hypothesis is true. That means you’re pretty darn certain of your result.

Unfortunately, most of that isn’t actually how p-values work. Wikipedia writes:

The p-value is the probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true.

In a nutshell, assuming there is no correlation between two variables, what’s the likelihood that they’ll appear as correlated as you observed in your experiment by chance alone? If your p-value is .05, that means it’s 5% likely that random chance caused your variables to be correlated. That is, one in every twenty studies (5%) that get a p-value of 0.05 will have found a correlation that doesn’t really exist.

Wikipedia's image explaining p-values.

To recap: p-values say nothing about your hypothesis. They say, assuming there is no real correlation, what’s the likelihood that your data show one anyway? Also, in the scholarly community, a result is considered “significant” if p is less than or equal to 0.05. Alright, I’m glad that’s out of the way, now we’re all on the same footing.

Publication Biases

The positive results bias, the first of many interrelated publication biases, simply states that positive results are more likely to get published then negative or inconclusive ones. Authors and editors will be more likely to submit and accept work if the results are significant (p < .05). The file drawer problem is the opposite effect: negative results are more likely to be stuck in somebody’s file drawer, never to see the light of day. HARKing (Hypothesizing After the Results Are Known), much like cherry-picking above, is when, if during the course of a study many trials and analyses occur, only the “significant” ones are ever published.

Let’s begin with HARKing. Recall that a p-value is (basically) the likelihood that an effect occurred by chance alone. If one research project consisted of 100 different trials and analyses, if only 5 of them yielded significant results pointing toward the author’s hypothesis, those 5 analyses likely occurred by chance. They could still be published (often without the researcher even realizing they were cherry-picking, because obviously non-fruitful analyses might be stopped before they’re even finished). Thus, again, more positive results are published than perhaps there ought to be.

Let’s assume some people are perfect in every way, shape, and form. Every single one of their studies is performed with perfect statistical rigor, and all of their results are sound. Again, however, they only publish their positive results – the negative ones are kept in the file drawer. Again, more positive results are being published than being researched.

Who cares? So what that we’re only seeing the good stuff?

The problem is that, using common significance testing of p < 0.05, 5% of published, positive results ought to have occurred by chance alone. However, since we cannot see the studies that haven’t been published because their results were negative, those 5% studies that yielded correlations where they should not have are given all the scholarly weight. One hundred small studies are done on the efficacy of some medicine for some disease; only five by chance find some correlation – they are published. Let’s be liberal, and say another three are published saying there was no correlation between treatment and cure. Thus, an outside observer will see that the evidence is stacked in the favor of the (ineffectual) medication.

xkcd take on significance values. (comic 882)

The Decline Effect

A recent much-discussed article by John Lehrer, as well as countless studies by John Ioannidis and others, show two things: (1) a large portion of published findings are false (some of the reasons are shown above). (2) The effects of scientific findings seem to decline. A study is published, showing a very noticeable effect of some medicine curing a disease, and further tests tend show that very noticeable effect declining sharply. (2) is mostly caused by (1). Much ink (or blood) could be spilled discussing this topic, but this is not the place for it.

Biases! Everywhere!

So there are a lot of biases in rigorous quantitative studies. Why should humanists care? We’re aware that people are not perfect, that research is contingent, that we each bring our own subjective experiences to the table, and they shape our publications and our outlooks, and none of those are necessarily bad things.

The issues arise when we start using statistics, or algorithms derived using statistics, and other methods used by our quantitative brethren. Make no mistake, our qualitative assessments are often subject to the same biases, but it’s easy to write reflexively on one’s own position when they are only one person, one data-point. In the age of Big Data, with multiplying uncertainties for any bit of data we collect, it is far easier to lose track of small unknowns in the larger picture. We have the opportunity of learning from past mistakes so we can be free to make mistakes of our own.


Ioannidis’ most famous article is, undoubtedly, the polemic “Why Most Published Research Findings Are False.” With a statement like that, what hope is there? Ioannidis himself has some good suggestions, and there are many floating around out there; as with anything, the first step is becoming cognizant of the problems, and the next step is fixing them. Digital humanities may be able to avoid inheriting these problems entirely, if we’re careful.

We’re already a big step ahead of the game, actually, because of the nearly nonsensical volumes of tweets and blog posts on nascent research.  In response to publication bias and the file drawer problem, many people suggest a authors submit their experiment to a registry before they begin their research. That way, it’s completely visible what experiments on a subject have been run that did not yield positive results, regardless of whether they eventually became published. Digital humanists are constantly throwing out ideas and preliminary results, which should help guard against misunderstandings through publication bias. We have to talk about all the effort we put into something, especially when nothing interesting comes out of it. The fact that some scholar felt there should be something interesting, and there wasn’t, is itself interesting.

At this point, “replication studies” means very little in the humanities, however if we begin heading down the road where replication studies become more feasible, our journals will need to be willing to accept them just as they accept novel research. Funding agencies should also be just as willing to fund old, non-risky continuation research as they are the new exciting stuff.

Other institutional changes needed for us to guard against this sort of thing is open access publications (so everyone draws inferences from the same base set of research), tenure boards that accept negative research and exploratory research (again, not as large of an issue for the humanities), and restructured curricula that teach quantitative methods and their pitfalls, especially statistics.

On the ground level, a good knowledge of statistics (especially Bayesian statistics, doing away with p-values entirely) will be essential as more data becomes available to us. When running analysis on data, to guard against coming up with results that appear by random chance, we have to design an experiment before running it, stick to the plan, and publish all results, not just ones that fit our hypotheses. The false-positive psychology paper I mentioned above actually has a lot of good suggestions to guard against this effect:

  1. Authors must decide the rule for terminating data collection before data collection begins and report this rule in the article.
  2. Authors must collect at least 20 observations per cell or else provide a compelling cost-of-data-collection justification.
  3. Authors must list all variables collected in a study
  4. Authors must report all experimental conditions, including failed manipulations.
  5. If observations are eliminated, authors must also report what the statistical results are if those observations are included.
  6. If an analysis includes a covariate, authors must report the statistical results of the analysis without the covariate.
  1. Reviewers should ensure that authors follow the requirements.
  2. Reviewers should be more tolerant of imperfections in results.
  3. Reviewers should require authors to demonstrate that their results do not hinge on arbitrary analytic decisions.
  4. If justifications of data collection or analysis are not compelling, reviewers should require the authors to conduct an exact replication.

Going Forward

This list of problems and solutions is neither exhaustive nor representative. That is, there are a lot of biases out there unlisted, and not all the ones listed are the most prevalent. Gender and power biases come to mind, however they are well beyond anything I could intelligently argue, and there are issues of peer-review and retraction rates that are an entirely different can of worms.

Also, the humanities are simply different. We don’t exactly test hypothesis, we’re not looking for ground truths, and our publication criteria are very different from that of the natural and social sciences. It seems clear that the issues listed above will have some mapping on our own research going forward, but I make no claims at understanding exactly how or where. My hope in this blog post is to raise awareness of some of the more pressing concerns in quantitative studies that might have bearing on our own studies, so we can try to understand how they will be relevant to our own research, and how we might guard against it.