Acceptances to Digital Humanities 2015 (part 3)

tl;dr

There’s a disparity between gender diversity in authorship and attendance at DH2015; attendees are diverse, authors aren’t. That said, the geography of attendance is actually pretty encouraging this year. A lot of this work draws a project on the history of DH conferences I’m undertaking with the inimitable Nickoal Eichmann. She’s been integral on the research of everything you read about conferences pre-2013.

Diversity at DH2015: Preliminary Numbers

For those just joining us, I’m analyzing this year’s international Digital Humanities conference being held in Sydney, Australia (part 1, part 2). This is the 10th post in a series of reflective entries on Digital Humanities conferences, throughout which I explore the landscape of Digital Humanities as it is represented by the ADHO conference. There are other Digital Humanities (a great place to start exploring them in Alex Gil’s arounddh), but since this is the biggest event, it’s also an integral reflection on our community to the public and non-DH academic world.

Map from Around DH in 80 Days.
Figure 1. Map from Around DH in 80 Days.

If the DH conference is our public face, we all hope it does a good job of representing our constituent parts, big or small. It does not. The DH conference systematically underrepresents women and people from parts of the world that are not Europe or North America.

Until today, I wasn’t sure whether this was an issue of underrepresentation, an issue of lack of actual diversity among our constituents, or both. Today’s data have shown me it may be more underrepresentation than lack of diversity, although I can’t yet say anything with certainty without data from more conferences.

I come to this conclusion by comparing attendees to the conference to authors of presentations at the conference. My assumption is that if authorship and attendee diversity are equal, and both poor, then we have a diversity problem. If instead attendance is diverse but authorship is not, then we have a representation problem. It turns out, at least in this dataset, the latter is true. I’ve been able to reach the conclusion because the conference organizing committee (themselves a diverse, fantastic bunch) have published and made available the DH2015 attendance list.

Because this is an important subject, this post is more somber and more technically detailed than most others in this series.

Geography

The published Attendance List was nice enough to already attach country names to every attendee, so making an interactive map to attendees was a simple manner of cleaning the data (here it is as csv), aggregating it and plugging it into CartoDB.

Despite a lack of South American and African attendees, this is still a pretty encouraging map for DH2015, especially compared to earlier years. The geographic diversity of attendees is actually mirrored in the conference submissions (analyzed here), which to my mind means the ADHO decision to hold the conference somewhere other than North America or Europe succeeded in its goal of diversifying the organization. From what I hear, they hope to continue this trend by moving to a three-year rotation, between North America, Europe, and elsewhere. At least from this analysis, that’s a successful strategy.

DH submissions broken down by UN macro-continental regions.
Figure 2. DH submissions broken down by UN macro-continental regions (details in an earlier post).

If we look at the locations of authors at ADHO conferences from 2004-2013, we see a very different profile than is apparent this year in Sydney. The figure below, made by my collaborator Nickoal Eichmann, shows all author locations from ADHO conferences in this 10-year range.

ADHO conference author locations, 2004-2013. Figure by Nickoal Eichmann.
Figure 3. ADHO conference author locations, 2004-2013. Figure by Nickoal Eichmann.

Notice the difference in geographic profile from this year?

This also hides the sheer prominence of the Americas (really, just North America) at every single ADHO conference since 2004. The figure below shows the percentage of authors from different regions at DH2004-2013, with Europe highlighted in orange during the years the conference was held in Europe.

Geographic home of authors to ADHO conferences 2004-2013. Years when Europe hosted are highlighted in orange.
Figure 4. Geographic home of authors to ADHO conferences 2004-2013. Years when Europe hosted are highlighted in orange.

If you take a second to study this visualization, you’ll notice that with only one major exception in 2012, even when the conference was held in Europe, the majority of authors hailed from the Americas. That’s cray-cray, yo. Compare that to 2015 data from Figure 2; the Americas are still technically sending most of the authors, but the authorship pool is significantly more regionally diverse than the decade of 2004-2013.

Actually, even before the DH conference moved to Australia, we’ve been getting slightly more geographically diverse. Figure 5, below, shows a slight increase in diversity score from 2004-2013.

Regional diversity of authors at ADHO conferences, 2004-2013.
Figure 5. Regional diversity of authors at ADHO conferences, 2004-2013.

In sum, we’re getting better! Also, our diversity of attendance tends to match our diversity of authorship, which means we’re not suffering an underrepresentation problem on top of a lack of diversity. The lack of diversity is obviously still a problem, but it’s improving, and in no small part to the efforts of ADHO to move the annual conference further afield.

Historical Gender

Gravy train’s over, folks. We’re getting better with geography, sure, but what about gender? Turns out our gender representation in DH sucks, it’s always sucked, and unless we forcibly intervene, it’s likely to continue to suck.

We’ve probably inherited our gender problem from computer science, which is weird, because such a large percentage of leadership in DH organizations, committees, and centers are women. What’s more, the issue isn’t that women aren’t doing DH, it’s that they’re not being well-represented at our international conference. Instead they’re going to other conferences which are focused on diversity, which as Jacqueline Wernimont points out, is less than ideal.

So what’s the data here? Let’s first look historically.

Gender ratio of authors to presentations at DH2004-DH2013. First authorship ratio is in red.
Figure 6. Gender ratio of authors to presentations at DH2004-DH2013. First authorship ratio is in red. In collaboration with Nickoal Eichmann.

Figure 6 shows percentage of women authors at DH2004-DH2013. The data were collected in collaboration with Nickoal Eichmann. 1

Notice the alarming tendency for DH conference authorship to hover between 30-35% women. Women fair slightly better as first authors—that is to say, if a woman authors an ADHO presentation, they’re more likely to be a first author than a second or third. This matches well with the fact that a lot of the governing body of DH organizations are women, and yet the ratio does not hold in authorship. I can’t really hazard a guess as to why that is.

Gender in 2015

Which brings us to 2015 in Sydney. I was encouraged to see the organizing committee publish an attendance list, and immediately set out to find the gender distribution of attendees. 2 Hurray! I tweeted. About 46% of attendees to DH2015 were women. That’s almost 50/50!

Armed with the same hope I’ve felt all week (what with two fantastic recent Supreme Court decisions, a Papal decree on global warming, and the dropping of confederate flags all over the country), I set out to count gender among authors at DH2015.

Preliminary results show 34.6% 3 of authors at DH2015 are women. Status quo quo quo quo.

So how do we reconcile the fact that only 35% of authors at DH2015 are women, yet 46% of attendees are? I’m interpreting this to mean that we don’t have a diversity problem, but a representation problem; for some reason, though women comprise nearly half of active participants at DH conferences, they only comprise a third of what’s actually presented at them.

This representation issue is further reflected by the topical analysis of DH2015, which shows that only 10% of presentations are tagged as cultural studies, and only 1% as gender studies. Previous years show a similar low number for both topics. (It’s worth noting that cultural studies tend to have a slightly lower-than-average acceptance rate, while gender studies has a slightly higher-than-average acceptance rate. Food for thought.)

Given this, how do we proceed? At an individual level, obviously, people are already trying to figure out paths forward, but what about at the ADHO level? Their efforts, and efforts of constituent members, have been successful at improving regional diversity at our flagship annual event. What sort of intervention can we create to similarly improve our gender representation problems? Hopefully comments below, or Twitter conversation, might help us collaboratively build a path forward, or offer suggestions to ADHO for future events. 4

Stay-tuned for more DH2015 analyses, and in the meantime, keep on fighting the good fight. These are problems we can address as a community, and despite our many flaws, we can actually be pretty good at changing things for the better when we notice our faults.

Notes:

  1. It’s worth noting we made a lot of simplifying assumptions that  we very much shouldn’t have, as Miriam Posner so eloquently pointed out with regards to Getty’s Union List of Author Names.

    We labeled authors as male, female, or unknown/other. We did not encode changes of author gender over time, even though we know of at least a few authors in the dataset for whom this would apply. We hope to remedy this issue in the near future by asking authors themselves to help us with identification, and we ourselves at least tried to be slightly more sensitive by labeling author gender by hand, rather than by using an algorithm to guess based on the author’s first name.

    This series of choices was problematic, but we felt it was worth it as a first pass as a vehicle to point out bias and lack of representation in DH, and we hope you all will help us improve our very rudimentary dataset soon.

  2. This is an even more problematic analysis than that of conference authorship. I used Lincoln Mullen’s fabulous gender guessing library in R, which guesses gender based on first names and statistics from US Social Security data, but obviously given the regional diversity of the conference, a lot of its guesses are likely off. As with the above data, we hope to improve this set as time goes on.
  3. Very preliminary, but probably not far off; again using Lincoln Mullen’s R library.
  4. Obviously I’m far from the first to come to this conclusion, and many ADHO committee members are already working on this problem (see GO::DH), but the more often we point out problems and try to come up with solutions, the better.

Acceptances to Digital Humanities 2015 (part 2)

Had enough yet? Too bad! Full-ahead into my analysis of DH2015, part of my 6,021-part series on DH conference submissions and acceptances. If you want more context, read the Acceptances to DH2015 part 1.

tl;dr

This post’s about the topical coverage of DH2015 in Australia. If you’re curious about how the landscape compares to previous years, see this post. You’ll see a lot of text, literature, and visualizations this year, as well as archives and digitisation projects. You won’t see a lot of presentations in other languages, or presentations focused on non-text sources. Gender studies is pretty much nonexistent. If you want to get accepted, submit pieces about visualization, text/data, literature, or archives. If you want to get rejected, submit pieces about pedagogy, games, knowledge representation, anthropology, or cultural studies.

Topical analysis

I’m sorry. This post is going to contain a lot of giant pictures, because I’m in the mountains of Australia and I’d much rather see beautiful vistas than create interactive visualizations in d3. Deal with it, dweebs. You’re just going to have to do a lot of scrolling down to see the next batch of text.

This year’s conference presents a mostly-unsurprising continuations of the status quo (see 2014’s and 2013’s topical landscapes). Figure 1, below, shows the top author-chosen topic words of DH2015, as a proportion of the total presentations at the conference. For example, an impressive quarter, 24%, of presentations at DH2015 are about “text analysis”. The authors were able to choose multiple topics for each presentation, which is why the percentages add up to way more than 100%.

Scroll down for the rest of the post.

Figure 1. Topical coverage of DH2015. Percent represents the % of presentations which authors have tagged with a certain topical keyword. Authors could tag multiple keywords per presentation.
Figure 1. Topical coverage of DH2015. Percent represents the % of presentations which authors have tagged with a certain topical keyword. Authors could tag multiple keywords per presentation.

Text analysis, visualization, literary studies, data mining, and archives take top billing. History’s a bit lower, but at least there’s more history than the abysmal showing at DH2013. Only a tenth of DH2015 presentations are about DH itself, which is maybe impressive given how much we talk about ourselves? (cf. this post)

As usual, gender studies representation is quite low (1%), as are foreign language presentations and presentations not centered around text. I won’t do a lot of interpretation this post, because it’d mostly be repeat of earlier years. At any rate, acceptance rate is a bit more interesting than coverage this time around. Figure 2 shows acceptance rates of each topic, ordered by volume. Figure 3 shows the same, sorted by acceptance rate.

The topics that appear most frequently at the conference are on the far left, and the red line shows the percent of submitted articles that will be presented at DH2015. The horizontal black line is the overall acceptance rate to the conference, 72%, just to show which topics are above or below average.

Figure 2. Acceptance rates of topics to DH2015, sorted by volume.
Figure 2. Acceptance rates of topics to DH2015, sorted by volume. Click to enlarge.
Figure 2. Acceptance rates of topics to DH2015, sorted by acceptance rate. Click to enlarge.
Figure 3. Acceptance rates of topics to DH2015, sorted by acceptance rate. Click to enlarge.

Notice that all the most well-represented topics at DH2015 have a higher-than-average acceptance rate, possibly suggesting a bit of path-dependence on the part of peer reviewers or editors. Otherwise, it could mean that, since a majority peer reviewers were also authors in the conference, and since (as I’ve shown) the majority of authors have a leaning toward text, lit, and visualization, it’s also what they’re likely to rate highly in peer review.

The first dips we see under the average acceptance rate is “Interdisciplinary Studies” and “Historical Studies” (☹), but the dips aren’t all that low, and we ought not to read too much into it without comparing it to earlier conferences. More significant are the low rates for “Cultural Studies”, and even more than that are the two categories on Teaching, Pedagogy, and Curriculum. Both categories’ acceptance rates are about 20% under the average, and although they’re obviously correlated with one another, the acceptance rates are similar to 2014 and 2013. In short, DH peer reviewers or editors are more unlikely to accept submissions on pedagogy than on most other topics, even though they sometimes represent a decent chunk of submissions.

Other low points worth pointing out are “Anthropology” (huh, no ideas there), “Games and Meaningful Play” (that one came as a surprise), and “Other” (can’t help you here). Beyond that, the submission counts are too low to read any meaningful interpretations into the data. The Game Studies dip is curious, and isn’t reflected in earlier conferences, so it could just be noise for 2015. The low acceptance rates in Anthropology are consistent 2013-2015, and it’d be worth looking more into that.

Topical Co-Occurrence, 2013-2015

Figure 4, below, shows how topics appear together on submissions to DH2013, DH2014, and DH2015. Technically this has nothing to do with acceptances, and little to do with this year specifically, but the visualization should provide a little context to the above analysis. Topics connect to one another if they appear on a submission together, and the line connecting them gets thicker the more connections two topics share.

Figure 4. Topical co-occurrence, 2013-2015. Click to enlarge.
Figure 4. Topical co-occurrence, 2013-2015. Click to enlarge.

Although the “Interdisciplinary Collaboration” topic has a low acceptance rate, it understandably ties the network together; other topics that play a similar role are “Visualization”, “Programming”, “Content Analysis”, “Archives”, and “Digitisation”. All unsurprising for a conference where people come together around method and material. In fact, this reinforces our “DH identity” along those lines, at least insofar as it is represented by the annual ADHO conference.

There’s a lot to unpack in this visualization, and I may go into more detail in the next post. For now, I’ve got a date with the Blue Mountains west of Sydney.

Acceptances to Digital Humanities 2015 (part 1)

[Update!] Melissa Terras pointed out I probably made a mistake on 2015 long paper -> short paper numbers. I checked, and she was right. I’ve updated the figures accordingly.

tl;dr

Part 1 is about sheer numbers of acceptances to DH2015 and comparisons with previous years. DH is still growing, but the conference locale likely prohibited a larger conference this year than last. Acceptance rates are higher this year than previous years. Long papers still reign supreme. Papers with more authors are more likely to be accepted.

Introduction

It’s that time of the year again, when all the good little boys, girls, and other genders of DH gather around the scottbot irregular in pointless meta-analysis quiet self-reflection. As most of you know, the 2015 Digital Humanities conference occurs next week in Sydney, Australia. They’ve just released the final program, full of pretty exciting work, which means I can compare it to my analysis of submissions to DH2015 (1, 2, & 3) to see how DH is changing, how work gets accepted or rejected, etc. This is part of my series on analyzing DH conferences.

Part 1 will focus on basic counts, just looking at percentages of acceptance and rejection by the type of presentation, and comparing it with previous years. Later posts will cover topical, gender, geography, and kangaroos. NOTE: When I say “acceptances”, I really mean “presentations that appear on the final program.” More presentations were likely accepted and withdrawn due to the expense of traveling to Australia, so take these numbers with appropriate levels of skepticism. 1

Volume

Around 270 papers, posters, and workshops are featured in this year’s conference program, down from last year’s ≈350 but up from DH2013’s ≈240. Although this is the first conference since 2010 with fewer presentations than the previous year’s, I suspect this is due largely to geographic and monetary barriers, and we’ll see a massive uptick next year in Poland and the following in (probably) North America. Whether or not the trend will continue to increase in 2018’s Antarctic locale, or 2019’s special Lunar venue, has yet to be seen. 2

Annual presentations at DH conferences, compared to growth of DHSI in Victoria.
Annual presentations at DH conferences, compared to growth of DHSI in Victoria.

As you can see from the chart above, even given this year’s dip, both DH2015 and the annual DHSI event in Victoria reveals DH is still on the rise. It’s also worth noting that last year’s DHSI was likely the first where more people attended it than the international ADHO conference.

Acceptance Rates

A full 72% of submissions to DH2015 will be presented in Sydney next week. That’s significantly more inclusive than previous years: 59% of submitted manuscripts made it to DH2014 in Lausanne, and 64% to DH2013.

At first blush, the loss of exclusivity may seem a bad sign of a conference desperate for attendees, but to my mind the exact opposite is true: this is a great step forward. Conference peer review & acceptance decisions aren’t particularly objective, so using acceptance as a proxy for quality or relevance is a bit of a misdirection. And if we can’t aim for consistent quality or relevance in the peer review process, we ought to aim at least for inclusivity, or higher acceptance rates, and let the participants themselves decide what they want to attend.

Form

Acceptance rates broken down by form (panel, poster, short paper, long paper) aren’t surprising, but are worth noting.

  • 73% of submitted long papers were accepted, but only 45% of them were accepted as long papers. The other 28% were accepted as posters or short papers.
  • 61% of submitted short papers were accepted, but only 51% as short papers; the other 10% became posters.
  • 85% of posters were accepted, all of them as posters.
  • 85% of panels were accepted, but one of them was accepted as a long paper.
  • A few papers/panels were converted into workshops.
How submitted articles eventually were rejected or accepted. (e.g. 45% of submitted long papers were accepted as long papers, 14% as short papers, 15% as posters, and 27% were rejected.)

Weirdly, short papers tend to have a lower acceptance rate than long papers over the last three years. I think that’s because if a long paper is rejected, it’s usually further along in the process enough that it’s more likely to be secondarily accepted-as-a-poster, but even that doesn’t account for the entire differential in the acceptance rate. Anyone have any thoughts on this?

Looking over time, we see an increasingly large slice of the DH conference pie is taken up by long papers. My guess is this is just a natural growth as authors learn the difference between long and short papers, a distinction which was only introduced relatively recently.

This is simply wrong with the updated data (tip of the hat to Melissa Terras for pointing it out); the ratio of long papers to short papers is still in flux. My “guess” from earlier was just that, a post-hoc explanation attached to an incorrect analysis. Matthew Lincoln has a great description about why we should be wary of these just-so stories. Go read it.

A breakdown of presentation forms at the last three DH conferences.

The breakdown of acceptance rates for each conference isn’t very informative, due in part to the fact I only have the last three years. In another few years this will probably become interesting, but for those who just can’t get enough o’ them sweet sweet numbers, here they are, special for you:

Breakdown of conference acceptances 2013-2015. The right-most column shows the percent of, for example, long papers that were not only accepted, but accepted AS long papers. Yellow rows are total acceptance rates per year.

Authorship

DH is still pretty single-author-heavy. It’s getting better; over the last 10 years we’ve seen an upward trend in number of authors per paper (more details in a future blog post), but the last three years have remained pretty stagnant. This year, 35% of presentations & posters will be by a single author, 25% by two authors, 13% by 3 authors, and so on down the line. The numbers are unremarkably consistent with 2013 and 2014.

Percent of accepted presentations with a certain number of co-authors in a given year. (e.g. 35% of presentations in 2015 were single-authored.)
Percent of accepted presentations with a certain number of co-authors in a given year. (e.g. 35% of presentations in 2015 were single-authored.)

We do however see an interesting trend in acceptance rates by number of authors. The more authors on your presentation, the more likely your presentation is to be accepted. This is true of 2013, 2014, and 2015. Single-authored works are 54% likely to be accepted, while works authored by two authors are 67% likely to be accepted. If your submission has more than 7 authors, you’re incredibly unlikely to get rejected.

Acceptance rates by number of authors, 2013-2015. The more authors, the more likely a submission will be accepted.
Acceptance rates by number of authors, 2013-2015. The more authors, the more likely a submission will be accepted.

Obviously this is pure description and correlation; I’m not saying multi-authored works are higher quality or anything else. Sometimes, works with more authors simply have more recognizable names, and thus are more likely to be accepted. That said, it is interesting that large projects seem to be favored in the peer review process for DH conferences.

Stay-tuned for parts 2, π, 16, and 4, which will cover such wonderful subjects as topicality, gender, and other things that seem neat.

Notes:

  1. The appropriate level of skepticism here is 19.27
  2. I hear Elon Musk is keynoting in 2019.

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.


Syuzhet

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.

Tsundoku

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).

s
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.

Notes:

  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

SpaceballsTheFlamethrower[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 http://arxiv.org/ftp/arxiv/papers/1502/1502.05256.pdf
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.

Notes:

  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.

Networks Demystified 9: Bimodal Networks

What do you think, is a year long enough to wait between Networks Demystified posts? I don’t think so, which is why it’s been a year and a month. Welcome back! A recent twitter back-and-forth culminated in a request for a discussion of “bimodal networks”, and my Networks Demystified series seemed like a perfect place for just such a discussion.

What’s a bimodal network, you ask? (Go on, ask aloud at your desk. Nobody will look at you funny, this is the age of Siri!) A bimodal network is one which connects two varieties of things. It’s also called a bipartite, 2-partite, or 2-mode network. A network of authors connected to the papers they write is bimodal, as are networks of books to topics, and people to organizations they are affiliated with.

A bimodal network.
A bimodal network.

This is a bimodal network which connects people and the clubs they belong to. Alice is a member of the Network Club and the We Love History Society, Bob‘s in the Network Club and the No Adults Allowed Club, and Carol‘s in the No Adults Allowed Club.

If this makes no sense, read my earlier Networks Demystified posts (the first two posts), or the our Historian’s Macroscope chapter, for a primer on networks. If it does make sense, excellent! The rest of this post will hopefully take you out of your comfort zone, but remain understandable to someone who doesn’t speak math.

k-partite Networks & Projections

Bimodal networks are part of a larger class of k-partite networks. Unipartite/unimodal networks have only one type of node (remember, nodes are the stuff being connected by the edges), bipartite/bimodal networks have two types of nodes, tripartite/trimodal networks have three types of node, and so on to infinity.

The most common networks you’ll see being researched are unipartite. Who follows whom on Twitter? Who’s writing to whom in early modern Europe? What articles cite which other articles? All are examples of unipartite networks. It’s important to realize this isn’t necessarily determined by the dataset, but by the researcher doing the studying. For example, you can use the same organization affiliation dataset to create a unipartite network of who is in a club with whom, or a bipartite network of which person is affiliated with each organization.

The same dataset used to create a unipartite (left) and a bipartite (right) network.
The same dataset used to create a unipartite (left) and a bipartite (right) network.

The above illustration shows the same dataset used to create a unimodal and a bimodal network. The process of turning a pre-existing bimodal network into a unimodal network is called a bimodal projection. This process collapses one set of nodes into edges connecting the other set. In this case, because Alice and Bob are both members of the Network Club, the Network Club collapses into becoming an edge between those two people. The No Adults Allowed Club collapses into an edge between Bob and Carol. Because only Alice is a member of the We Love History Society, it does not collapse into an edge connecting any people.

You can also collapse the network in the opposite direction, connecting organizations who share people. No Adults Allowed and Network Club would share an edge (Bob), as would Network Club and We Love History Society (Alice).

Why Bimodal Networks?

If the same dataset can be described with unimodal networks, which are less complex, why go to bi-, tri-, or multimodal? The answer to that is in your research question: different network representations suit different questions better.

Collaboration is a hot topic in bibliometrics. Who collaborates with whom? Why? Do your collaborators affect your future collaborations? Co-authorship networks are well-suited to some of these questions, since they directly connect collaborators who author a piece together. This is a unimodal network: I wrote The Historian’s Macroscope with Shawn Graham and Ian Milligan, so we draw an edge connecting each of us together.

Some of the more focused questions of collaboration, however, require a more nuanced view of the data. Let’s say you want to know how individual instances of collaboration affect individual research patterns going forward. In this case, you want to know more than the fact that I’ve co-authored two pieces with Shawn and Ian, and they’ve co-authored three pieces together.

For this added nuance, we can draw an edge from each of us to The Historian’s Macroscope (rather than each-other), then another set edges to the piece we co-authored in The Programming Historian, and a last set of edges going from Shawn and Ian to the piece they wrote in the Journal of Digital Humanities. That’s three people nodes and three publication nodes.

Scott, Ian, and Shawn's co-authorship network
Scott, Ian, and Shawn’s co-authorship network

Why Not Bimodal Networks?

Humanities data are often a rich array of node types: people, places, things, ideas, all connected to each other via a complex network. The trade-off is, the more complex and multimodal your dataset, the less you can reasonably do with it. This is one of the fundamental tensions between computational and traditional humanities. More categories lead to a richer understanding of the diversity of human experience, but are incredibly unhelpful when you want to count things.

Consider two pie-charts showing the religious makeup of the United States. The first chart groups together religions that fall under a similar umbrella, and the second does not. That is, the first chart groups religions like Calvinists and Lutherans together into the same pie slice (Protestants), and the second splits them into separate slices. The second, more complex chart obviously presents a richer picture of religious diversity in the United States, but it’s also significantly more difficult to read. It might trick you into thinking there are more Catholics than Protestants in the country, due to how the pie is split.

The same is true in network analysis. By creating a dataset with a hundred varieties of nodes, you lose your ability to see a bigger picture through meaningful aggregations.

Surely, you’re thinking, bimodal networks, with only two categories, should be fine! Wellllll, yes and no. You don’t bump into the same aggregation problem you do with very multimodal networks; instead, you bump into technical and mathematical issues. These issues are why I often warn non-technical researchers away from bimodal networks in research. They’re not theoretically unsound, they’re just difficult to work with properly unless you know what changes when you’re working with these complex networks.

The following section will discuss a few network metrics you may be familiar with, and what they mean for bimodal networks.

Network Metrics and Bimodality

The easiest thing to measure in a network is a node’s degree centrality. You’ll recall this is a measurement of how many edges are attached to a node, which gives a rough proxy for this concept we’ve come to call network “centrality“. It means different things depending on your data and your question: the most important or well-connected person in your social network; the point in the U.S. electrical grid which is most vulnerable to attack; the book that shares the most concepts with other books (the encyclopedia?); the city that the most traders pass through to get to their destination. These are all highly “central” in the networks they occupy.

A network with each node labeled with its degree centrality.
A network with each node labeled with its degree centrality, via Wikipedia.

Degree centrality is the easiest such proxy to compute: how many connections does a node have? The idea is that nodes that are more highly connected are more central. The assumption only goes so far, and it’s easy to come up with nodes that are central that do not have a  high degree, as with the network below.

The blue node is highly central, but only has a degree centrality of 3. [via]
The blue node is highly central, but only has a degree centrality of 3. [via]
That’s the thing with these metrics: if you know how they work, you know which networks they apply well to, and which they do not. If what you mean by “centrality” is “has more friends”, and we’re talking about a Facebook network, then degree centrality is a perfect metric for the job.

If what you mean is “an important stop for river trade”, and we’re talking about 12th century Russia, then degree centrality sucks. The below is an illustration of such a network by Pitts (1978):

Russian river trade routes. Numbers/nodes are cities, and edges are rivers between them.
Russian river trade routes. Numbers/nodes are cities, and edges are rivers between them.

Moscow is number 35, and pretty clearly the most central according to the above criteria (you’ll likely pass through it to reach other destinations). But it only has a degree centrality of four! Node 9 also has a degree centrality of four, but clearly doesn’t play as important a structural role as Moscow in this network.

We already see that depending on your question, your definitions, and your dataset, specific metrics will either be useful or not. Metrics may change meanings entirely from one network to the next – for example, looking at bimodal rather than unimodal networks.

Consider what degree centrality means for the Alice, Bob, and Carol’s bimodal affiliation network above, where each is associated with a different set of clubs. Calculate the degree centralities in your head (hint: if you can’t, you haven’t learned what degree centrality means yet. Try again.).

Alice and Bob have a degree of 2, and Carol has a degree of 1. Is this saying anything about how central each is to the network? Not at all. Compare this to the unimodal projection, and you’ll see Bob is clearly the only structurally central actor in the network. In a bimodal network, degree centrality is nothing more than a count of affiliations with the other half of the network. It is much less likely to tell you something structurally useful than if you were looking at a unimodal network.

Consider another common measurement: clustering coefficient. You’ll recall that a node’s local clustering coefficient is the extent to which its neighbors are neighbors to one another. If all my Facebook friends know each other, I have a high clustering coefficient; if none of them know each other, I have a low clustering coefficient. If all of a power plant’s neighbors directly connect to one another, it has a high clustering coefficient, and if they don’t, it has a low clustering coefficient.

Clustering coefficient, from largest to smallest. [via]
Clustering coefficient, from largest to smallest. [via]
This measurement winds up being important for all sorts of reasons, but one way to interpret its meaning is as a proxy for the extent to which a node bridges diverse communities, the extent to which it is an important broker. In the 17th century, Henry Oldenburg was an important broker between disparate scholarly communities, in that he corresponded with people all across Europe, many of whom would never meet one another. The fact that they’d never meet is represented by the local clustering coefficient. It’s low, so we know his neighbors were unlikely to be neighbors of one another.

You can get creative (and network scientists often are) with what this metric means in the context of your own dataset. As long as you know how the algorithm works (taking the fraction of neighbors who are neighbors to one another), and the structural assumptions underlying your dataset, you can argue why clustering coefficient is a useful proxy for answering whatever question you’re asking.

Your argument may be pretty good, like if you say clustering coefficient is a decent (but not the best) proxy for revealing nodes that broker between disparate sections of a unimodal social network. Or your argument may be bad, like if you say clustering coefficient is a good proxy for organizational cohesion on the bimodal Alice, Bob, and Carol affiliation network above.

A thorough glance at the network, and a realization of our earlier definition of clustering coefficient (taking the fraction of neighbors who are neighbors to one another), should reveal why this is a bad justification. Alice’s clustering coefficient is zero. As is Bob’s. As is the Network Club’s. Every node has a clustering coefficient of zero, because no node’s neighbors connect to each other. That’s just the nature of bimodal networks: they connect across, rather than between, modes. Alice can never connect directly with Bob, and the Network Club can never connect directly with the We Love History Society.

Bob’s neighbors (the organizations) can never be neighbors with each other. There will never be a clustering coefficient as we defined it.

In short, the simplest definition of clustering coefficient doesn’t work on bimodal networks. It’s obvious if you know how your network works, and how clustering coefficient is calculated, but if you don’t think about it before you press the easy “clustering coefficient” button in Gephi, you’ll be lead astray.

Gephi doesn’t know if your network is bimodal or unimodal or ∞modal. Gephi doesn’t care. Gephi just does what you tell it to. You want Gephi to tell you the degree centralities in a bimodal network? Here ya go! You want it to give you the local clustering coefficients of nodes in a bimodal network? Voila! Everything still works as though these metrics would produce meaningful, sensible results.

But they won’t be meaningful on your network. You need to be your own network’s sanity check, and not rely on software to tell you something’s a bad idea. Think about your network, think about your algorithm, and try to work through what an algorithm means in the context of your data.

Using Bimodal Networks

This doesn’t mean you should stop using bimodal networks. Most of the easy network software out there comes with algorithms made for unimodal networks, but other algorithms exist and are available for more complex networks. Very occasionally, but by no means always, you can project your bimodal network to a unimodal network, as described above, and run your unimodal algorithms on that new network projection.

There are a number of times when this doesn’t work well. At 2,300 words, this tutorial is already too long, so I’ll leave thinking through why as an exercise for the reader. It’s less complicated than you’d expect, if you have a pen and paper and know how fractions work.

The better solution, usually, is to use an algorithm meant for bi- or multimodal networks. Tore Opsahl has put together a good primer on the subject with regard to clustering coefficient (slightly mathy, but you can get through it with ample use of Wikipedia). He argues that projection isn’t an optimal solution, but gives a simple algorithm for a finding bimodal clustering coefficients, and directions to do so in R. Essentially the algorithm extends the visibility of the clustering coefficient, asking whether a node’s neighbors 2 hops away can reach the others via 2 hops as well. Put another way, I don’t want to know what clubs Bob belongs to, but rather whether Alice and Carol can also connect to one another through a club.

It’s a bit difficult to write without the use of formulae, but looking at the bimodal network and thinking about what clustering coefficient ought to mean should get you on the right track.

Bimodal networks aren’t an unsolved problem. If you search Google Scholar for bimodal, bipartite, and 2-mode networks, you’ll discover all sorts of clever methods for analyzing bimodal networks, including some great introductory texts by Borgatti and Everett.

The issue is there aren’t easy solutions through platforms like Gephi, and that’s probably on us as Digital Humanists.  I’ve found that DHers are much more likely to have bi- or multimodal datasets than most network researchers. If we want to be able to analyze them easily, we need to start developing our own plugins to Gephi, or our own tools, to do so. Push-button solutions are great if you know what’s happening when you push the button.

So let this be an addendum to my previous warnings against using bimodal networks: by all means, use them, but make sure you really think about the algorithms and your data, and what any given metric might imply when run on your network specifically. There are all sorts of free resources online you can find by googling your favorite algorithm. Use them.


For more information, read up on specific algorithms, methods, interpretations, etc. for two-mode networks from Tore Opsahl.

 

Digital History, Saturn’s Rings, and the Battle of Trafalgar

History and astronomy are a lot alike. When people claim history couldn’t possibly be scientific, because how can you do science without direct experimentation, astronomy should be used as an immediate counterexample.

Astronomers and historians both view their subjects from great distances; too far to send instruments for direct measurement and experimentation. Things have changed a bit in the last century for astronomy, of course, with the advent of machines sensitive enough to create earth-based astronomical experiments. We’ve also built ships to take us to the farthest reaches, for more direct observations.

Voyager 1 Spacecraft, on the cusp of interstellar space. [via]
Voyager 1 Spacecraft, on the cusp of interstellar space. [via]
It’s unlikely we’ll invent a time machine any time soon, though, so historians are still stuck looking at the past in the same way we looked at the stars for so many thousands of years: through a glass, darkly. Like astronomers, we face countless observational distortions, twisting the evidence that appears before us until we’re left with an echo of a shadow of the past. We recreate the past through narratives, combining what we know of human nature with the evidence we’ve gathered, eventually (hopefully) painting ever-clearer pictures of a time we could never touch with our fingers.

Some take our lack of direct access as a good excuse to shake away all trappings of “scientific” methods. This seems ill-advised. Retaining what we’ve learned over the past 50 years about how we construct the world we see is important, but it’s not the whole story, and it’s got enough parallels with 17th century astronomy that we might learn some lessons from that example.

Saturn’s Rings

In the summer 1610, Galileo observed Saturn through a telescope for the first time. He wrote with surprise that

Galileo's observation of Saturn through a telescope, 1610. [via]
Galileo’s Saturn. [via]

the star of Saturn is not a single star, but is a composite of three, which almost touch each other, never change or move relative to each other, and are arranged in a row along the zodiac, the middle one being three times larger than the two lateral ones…

This curious observation would take half a century to resolve into what we today see as Saturn’s rings. Galileo wrote that others, using inferior telescopes, would report seeing Saturn as oblong, rather than as three distinct spheres. Low and behold, within months, several observers reported an oblong Saturn.

Galileo's Saturn in 1616.
Galileo’s Saturn in 1616.

What shocked Galileo even more, however, was an observation two years later when the two smaller bodies disappeared entirely. They appeared consistently, with every observation, and then one day poof they’re gone. And when they eventually did come back, they looked remarkably odd.

Saturn sometimes looked as though it had “handles”, one connected to either side, but the nature of those handles were unknown to Galileo, as was the reason why sometimes it looked like Saturn had handles, sometimes moons, and sometimes nothing at all.

Saturn was just really damn weird. Take a look at these observations from Gassendi a few decades later:

Gassendi's Saturn [via]
Gassendi’s Saturn [via]
What the heck was going on? Many unsatisfying theories were put forward, but there was no real consensus.

Enter Christiaan Huygens, who in the 1650s was fascinated by the Saturn problem. He believed a better telescope was needed to figure out what was going on, and eventually got some help from his brother to build one.

The idea was successful. Within short order, Huygens developed the hypothesis that Saturn was encircled by a ring. This explanation, along with the various angles we would be viewing Saturn and its ring from Earth, accounted for the multitude of appearances Saturn could take. The figure below explains this:

Huygens' Saturn [via]
Huygens’ Saturn [via]
The explanation, of course, was not universally accepted. An opposing explanation by an anti-Copernican Jesuit contested that Saturn had six moons, the configuration of which accounted for the many odd appearances of the planet. Huygens countered that the only way such a hypothesis could be sustained would be with inferior telescopes.

While the exact details of the dispute are irrelevant, the proposed solution was very clever, and speaks to contemporary methods in digital history. The Accademia del Cimento devised an experiment that would, in a way, test the opposing hypotheses. They built two physical models of Saturn, one with a ring, and one with six satellites configured just-so.

The Model of Huygens' Saturn [via]
The Model of Huygens’ Saturn [via]
In 1660, the experimenters at the academy put the model of a ringed Saturn at the end of a 75-meter / 250-foot hallway. Four torches illuminated the model but were obscured from observers, so they wouldn’t be blinded by the torchlight.  Then they had observers view the model through various quality telescopes from the other end of the hallway. The observers were essentially taken from the street, so they wouldn’t have preconceived notions of what they were looking at.

Depending on the distance and quality of the telescope, observers reported seeing an oblong shape, three small spheres, and other observations that were consistent with what astronomers had seen. When seen through a glass, darkly, a ringed Saturn does indeed form the most unusual shapes.

In short, the Accademia del Cimento devised an experiment, not to test the physical world, but to test whether an underlying reality could appear completely different through the various distortions that come along with how we observe it. If Saturn had rings, would it look to us as though it had two small satellites? Yes.

This did not prove Huygens’ theory, but it did prove it to be a viable candidate given the observational instruments at the time. Within a short time, the ring theory became generally accepted.

The Battle of Trafalgar

So what’s Saturn’s ring have to do with the price of tea in China? What about digital history?

The importance is in the experiment and the model. You do not need direct access to phenomena, whether they be historical or astronomical, to build models, conduct experiments, or generally apply scientific-style methods to test, elaborate, or explore a theory.

In October 1805, Lord Nelson led the British navy to a staggering victory against the French and Spanish during the Napoleonic Wars. The win is attributed to Nelson’s unusual and clever battle tactics of dividing his forces in columns perpendicular to the single line of the enemy ships. Twenty-seven British ships defeated thirty-three Franco-Spanish ones. Nelson didn’t lose a single British ship lost, while the Franco-Spanish fleet lost twenty-two.

Horatio Nelson [via]
Horatio Nelson [via]
But let’s say the prevailing account is wrong. Let’s say, instead, due to the direction of the wind and the superior weaponry of the British navy, victory was inevitable: no brilliant naval tactician required.

This isn’t a question of counterfactual history, it’s simply a question of competing theories. But how can we support this new theory without venturing into counterfactual thinking, speculation? Obviously Nelson did lead the fleet, and obviously he did use novel tactics, and obviously a resounding victory ensued. These are indisputable historical facts.

It turns out we can use a similar trick to what the Accademia del Cimento devised in 1660: pretend as though things are different (Saturn has a ring; Nelson’s tactics did not win the battle), and see whether our observations would remain the same (Saturn looks like it is flanked by two smaller moons; the British still defeated the French and Spanish).

It turns out, further, that someone’s already done this. In 2003, two Italian physicists built a simulation of the Battle of Trafalgar, taking into account details of the ships, various strategies, wind direction, speed, and so forth. The simulation is a bit like a video game that runs itself: every ship has its own agency, with the ability to make decisions based on its environment, to attack and defend, and so forth.  It’s from a class of simulations called agent-based models.

When the authors directed the British ships to follow Lord Nelson’s strategy, of two columns, the fleet performed as expected: little loss of life on behalf of the British, major victory, and so forth. But when they ran the model without Nelson’s strategy, a combination of wind direction and superior British firepower still secured a British victory, even though the fleet was outnumbered.

…[it’s said] the English victory in Trafalgar is substantially due to the particular strategy adopted by Nelson, because a different plan would have led the outnumbered British fleet to lose for certain. On the contrary, our counterfactual simulations showed that English victory always occur unless the environmental variables (wind speed and direction) and the global strategies of the opposed factions are radically changed, which lead us to consider the British fleet victory substantially ineluctable.

Essentially, they tested assumptions of an alternative hypothesis, and found those assumptions would also lead to the observed results. A military historian might (and should) quibble with the details of their simplifying assumptions, but that’s all part of the process of improving our knowledge of the world. Experts disagree, replace simplistic assumptions with more informed ones, and then improve the model to see if the results still hold.

The Parable of the Polygons

This agent-based approach to testing theories about how society works is exemplified by the Schelling segregation model. This week the model shot to popularity through Vi Hart and Nicky Case’s Parable of the Polygons, a fabulous, interactive discussion of some potential causes of segregation. Go click on it, play through it, experience it. It’s worth it. I’ll wait.

Finished? Great! The model shows that, even if people only move homes if less than 1/3rd of their neighbors are the same color that they are, massive segregation will still occur. That doesn’t seem like too absurd a notion: everyone being happy with 2/3rds of their neighbors as another color, and 1/3rd as their own, should lead to happy, well-integrated communities, right?

Wrong, apparently. It turns out that people wanting 33% of their neighbors to be the same color as they are is sufficient to cause segregated communities. Take a look at the community created in Parable of the Polygons under those conditions:

Parable of the Polygons
Parable of the Polygons

This shows that very light assumptions of racism can still easily lead to divided communities. It’s not making claims about racism, or about society: what it’s doing is showing that this particular model, where people want a third of their neighbors to be like them, is sufficient to produce what we see in society today. Much like Saturn having rings is sufficient to produce the observation of two small adjacent satellites.

More careful work is needed, then, to decide whether the model is an accurate representation of what’s going on, but establishing that base, that the model is a plausible description of reality, is essential before moving forward.

Digital History

Digital history is a ripe field for this sort of research. Like astronomers, we cannot (yet?) directly access what came before us, but we can still devise experiments to help support our research, in finding plausible narratives and explanations of the past. The NEH Office of Digital Humanities has already started funding workshops and projects along these lines, although they are most often geared toward philosophers and literary historians.

The person doing the most thoughtful theoretical work at the intersection of digital history and agent-based modeling is likely Marten Düring, who is definitely someone to keep an eye on if you’re interested in this area. An early innovator and strong practitioner in this field is Shawn Graham, who actively blogs about related issues.  This technique, however, is far from the only one available to historians for devising experiments with the past. There’s a lot we can still learn from 17th century astronomers.

Submissions to Digital Humanities 2015 (pt. 3)

This is the third post in a three-part series analyzing submissions to the 2015 Digital Humanities conference in Australia. In parts 1 & 2, I covered submission volumes, topical coverage, and comparisons to conferences in previous years. This post will briefly address the geography of submissions, further exploring my criticism that this global-themed conference doesn’t feel so global after all. My geographic analysis shows the conference to be more international than I originally suspected.

I’d like to explore whether submissions to DH2015 are more broad in scope than those to previous conferences as well, but given time constraints, I’ll leave that exploration to a later post in this series, which has covered submissions and acceptances at DH conferences since 2013.

For this analysis, I looked at the universities of the submitting (usually lead) author on every submission, and used a geocoder to extract country, region, and continent data for each given university. This means that every submission is attached to one and only one location, even if other authors are affiliated with other places. Not perfect, but good enough for union work. After the geocoding, I corrected the results by hand 1, and present those results here.

It is immediately apparent that the DH2015 authors represent a more diverse geographical distribution than those in previous years. DH2013 in Nebraska was the only conference of the three where over half of submissions were concentrated in one continental region, the Americas. The Switzerland conference in 2014 had a slightly more even regional distribution, but still had very few contributions (11%) from Asia or Oceania. Contrast these heavily skewed numbers against DH2015 in Australia, with a third of the contributions coming from Asia or Oceania.

DH submissions broken down by UN macro-continental regions.

The trend continues broken down by UN micro-continental regions. The trends are not unexpected, but they are encouraging. When the conference was in Switzerland, Northern and Western Europe were much more well-represented, as was (surprisingly?) Eastern Asia. This may present the case that Eastern Asia’s involvement in DH is on the rise even not taking into account conference locations. Submissions for 2015 in Sydney are well-represented by Australia, New Zealand, Eastern Asia, and even Eastern Europe and Southern Asia.

DH conferences broken down by % covered from region in a given year.
DH conferences broken down by % covered from region in a given year.

One trend is pretty clear: the dominance of North America. Even at its lowest point in 2015, authors from North America comprise over a third of submissions. This becomes even more stark in the animation below, on which every submitting author’s country is represented.

DH2013-2015 with dots sized by the percent coverage that year.
DH2013-2015 with dots sized by the percent coverage that year.

The coverage from the United States over the course of the last three years barely changes, and from Canada shrinks only slightly when the conference moves off of North America. The UK also pretty much retains its coverage 2013-2015, hovering around 10% of submissions. Everywhere else the trends are pretty clear: a slow move eastward as the conference moves east. It’ll be interesting to see how things change in Poland in 2016, and wherever it winds up going in 2017.

In sum, it turns out “Global Digital Humanities 2015” is, at least geographically, much more global than the conferences of the previous two years. While the most popular topics are pretty similar to those in earlier years, I haven’t yet done an analysis of the diversity of the less popular topics, and it may be that they actually prove more diverse than those in earlier years. I’ll save that analysis for when the acceptances come in, though.

Notes:

  1. It’s a small enough dataset. There’s 648 unique institutional affiliations listed on submissions from 2013-2015, which resolved to 49 unique countries in 14 regions on 4 continents.

Submissions to Digital Humanities 2015 (pt. 2)

Do you like the digital humanities? Me too! You better like it, because this is the 700th or so in a series of posts about our annual conference, and I can’t imagine why else you’d be reading it.

My last post went into some summary statistics of submissions to DH2015, concluding in the end that this upcoming conference, the first outside the Northern Hemisphere, with the theme “Global Digital Humanities”, is surprisingly similar to the DH we’ve seen before. This post will compare this year to submissions to the previous two conferences, in Switzerland and the Nebraska. Part 3 will go into some more detail of geography and globalizing trends.

I can only compare the sheer volume of submissions this year to 2013 and 2014, which is as far back as I’ve got hard data. As many pieces were submitted for DH2015 as were submitted for DH2013 in Nebraska – around 360. Submissions to DH2014 shot up to 589, and it’s not yet clear whether the subsequent dip is an accident of location (Australia being quite far away from most regular conference attendees), or whether this signifies the leveling out of what’s been fairly impressive growth in the DH world.

DH by volume, 1999-2014.  This chart shows how many DHSI workshops occurred per year (right axis), alongside how many pieces were actually presented at the DH conference annually (left axis). This year is not included because we don't yet know which submissions will be accepted.
DH by volume, 1999-2014. This chart shows how many DHSI workshops occurred per year (right axis), alongside how many pieces were actually presented at the DH conference annually (left axis). This year is not included because we don’t yet know which submissions will be accepted.

This graph shows a pretty significant recent upward trend in DH by volume; if acceptance rates to DH2015 are comparable to recent years (60-65%), then DH2015 will represent a pretty significant drop in presentation volume. My gut intuition is this is because of the location, and not a downward trend in DH, but only time will tell.

Replying to my most recent post, Jordan T. T-H commented on his surprise at how many single-authored works were submitted to the conference. I suggested this was of our humanistic disciplinary roots, and that further analysis would likely reveal a trend of increasing co-authorship. My prediction was wrong: at least over the last three years, co-authorship numbers have been stagnant.

This chart shows the that ~40% of submissions to DH conferences over the past three years have been single-authored.
This chart shows the that ~40% of submissions to DH conferences over the past three years have been single-authored.

Roughly 40% of submissions to DH conferences over the past three years have been single-authored; the trend has not significantly changed any further down the line, either. Nickoal Eichmann and I are looking into data from the past few decades, but it’s not ready yet at the time of this blog post. This result honestly surprised me; just from watching and attending conferences, I had the impression we’ve become more multi-authored over the past few years.

Topically, we are noticing some shifts. As a few people noted on Twitter, topics are not perfect proxies for what’s actually going on in a paper; every author makes different choices on how they they tag their submissions. Still, it’s the best we’ve got, and I’d argue it’s good enough to run this sort of analysis on, especially as we start getting longitudinal data. This is an empirical question, and if we wanted to test my assumption, we’d gather a bunch of DHers in a room and see to what extent they all agree on submission topics. It’s an interesting question, but beyond the scope of this casual blog post.

Below is the list of submission topics in order of how much topical coverage has changed since 2013. For example, this year 21% of submissions were tagged as involving Text Analysis. By contrast, only 15% were tagged as Text Analysis in 2013, resulting in a growth of 6% over the last two years. Similarly, this year Internet and World Wide Web studies comprised 7% of submissions, whereas that number was 12% in 2013, showing coverage shrunk by 5%. My more detailed evaluation of the results are below the figure.

dh-topicalchange-2015

We see, as I previously suggested, that Text Analysis (unsurprisingly) has gained a lot of ground. Given the location, it should be unsurprising as well that Asian Studies has grown in coverage, too. Some more surprising results are the re-uptake of Digitisation, which have been pretty low recently, and the growth of GLAM (Galleries, Libraries, Archives, Museums), which I suspect if we could look even further back, we’d spot a consistent upward trend. I’d guess it’s due to the proliferation of DH Alt-Ac careers within the GLAM world.

Not all of the trends are consistent: Historical Studies rose significantly between 2013 and 2014, but dropped a bit in submissions this year to 15%. Still, it’s growing, and I’m happy about that. Literary Studies, on the other hand, has covered a fifth of all submissions in 2013, 2014, and 2015, remaining quite steady. And I don’t see it dropping any time soon.

Visualizations are clearly on the rise, year after year, which I’m going to count as a win. Even if we’re not branching outside of text as much as we ought, the fact that visualizations are increasingly important means DHers are willing to move beyond text as a medium for transmission, if not yet as a medium of analysis. The use of Networks is also growing pretty well.

As Jacqueline Wernimont just pointed out, representation of Gender Studies is incredibly low. And, as the above chart shows, it’s even lower this year than it was in both previous years. Perhaps this isn’t so surprising, given the gender ratio of authors at DH conferences recently.

Gender ratio of authors at DH conferences 2010-2013. Women consistently represent a bit under a third of all authors.
Gender ratio of authors at DH conferences 2010-2013. Women consistently represent a bit under a third of all authors.

Some categories involving Maps and GIS are increasing, while others are decreasing, suggesting small fluctuations in labeling practices, but probably no significant upward or downward trend in their methodological use. Unfortunately, most non-text categories dropped over the past three years: Music, Film & Cinema Studies, Creative/Performing Arts, and Audio/Video/Multimedia all dropped. Image Studies grew, but only slightly, and its too soon to say if this represents a trend.

We see the biggest drops in XML, Encoding, Scholarly Editing, and Interface & UX Design. This won’t come as a surprise to anyone, but it does show how much the past generation’s giant (putting together, cleaning, and presenting scholarly collections) is making way for the new behemoth (analytics). Internet / World Wide Web is the other big coverage loss, but I’m not comfortable giving any causal explanation for that one.

This analysis offers the same conclusion as the earlier one: with the exception of the drop in submissions, nothing is incredibly surprising. Even the drop is pretty well-expected, given how far the conference is from the usual attendees. The fact that the status is pretty quo is worthy of note, because many were hoping that a global DH would seem more diverse, or appreciably different, in some way. In Part 3, I’ll start picking apart geographic and deeper topical data, and maybe there we’ll start to see the difference.

Submissions to Digital Humanities 2015 (pt. 1)

It’s that time of the year again! The 2015 Digital Humanities conference will take place next summer in Australia, and as per usual, I’m going to summarize what is being submitted to the conference and, eventually, how those submissions become accepted. Each year reviewers get the chance to “bid” on conference submissions, and this lets us get a peak inside the general trends in DH research. This post (pt. 1) will focus solely on this year’s submissions, and next post will compare them to previous years and locations.

It’s important to keep in mind that trends in the conference over the last three years may be temporal, geographic, or accidental. The 2013 conference took place in Nebraska, 2014 in Switzerland, 2015 in Australia, and 2016 is set to happen in Poland; it’s to be expected that regional differences will significantly inform who is submitting pieces and what topics will be discussed.

This year, 358 pieces were submitted to the conference (about as many as were submitted to Nebraska in 2013, but more on that in the follow-up post). As with previous years, authors could submit four varieties of works: long papers, short papers, posters, and panels / multi-paper sessions. Long papers comprised 54% of submissions, panels 4%, posters 15%, and short papers 30%.

In total, there were 859 named authors on submissions – this number counts authors more than once if they appear on multiple submissions. Of those, 719 authors are unique. 1 Over half the submissions are multi-authored (58%), with 2.4 authors per submission on average, a median of 2 authors per submission, and a max of 10 authors on one submission. While the majority of submissions included multiple authors, the sheer number of single-authored papers still betrays the humanities roots of DH. The histogram is below.

A histogram of authors-per-submission.
A histogram of authors-per-submission.

As with previous years, authors may submit articles in any of a number of languages. The theme of this year’s conference is “Global Digital Humanities”, but if you expected a multi-lingual conference, you might be disappointed. Of the 358 submissions, 353 are in English. The rest are in French (2), Italian (2), and German (1).

Submitting authors could select from a controlled vocabulary to tag their submissions with topics. There were 95 topics to choose from, and their distribution is not especially surprising. Two submissions each were tagged with 25 topics, suggesting they are impressively far reaching, but for the most part submissions stuck to 5-10 topics. The breakdown of submissions by topic is below, where the percentage represents the percentage of submissions which are tagged by a specific topic. My interpretation is below that.

Percentage of submissions tagged with a specific topic.
Percentage of submissions tagged with a specific topic.

A full 21% of submissions include some form of Text Analysis, and a similar number claim Text or Data Mining as a topic. Other popular methodological topics are Visualizations, Network Analysis, Corpus Analysis, and Natural Language Processing. The DH-o-sphere is still pretty text-heavy; Audio, Video, and Multimedia are pretty low on the list, GIS even lower, and Image Analysis (surprisingly) even lower still. Bibliographic methods, Linguistics, and other approaches more traditionally associated with the humanities appear pretty far down the list. Other tech-y methods, like Stylistics and Agent-Based Modeling, are near the bottom. If I had to guess, the former is on its way down, and the latter on its way up.

Unsurprisingly, regarding disciplinary affiliations, Literary Studies is at the top of the food chain (I’ll talk more about how this compares to previous years in the next post), with Archives and Repositories not far behind. History is near the top tier, but not quite there, which is pretty standard. I don’t recall the exact link, but Ben Schmidt argued pretty convincingly that this may be because there are simply fewer new people in History than in Literary Studies. Digitization seems to be gaining some ground its lost in the previous years. The information science side (UX Design, Knowledge Representation, Information Retrieval, etc.) seems reasonably strong. Cultural Studies is pretty well-represented, and Media Studies, English Studies, Art History, Anthropology, and Classics are among the other DH-inflected communities out there.

Thankfully we’re not completely an echo chamber yet; only about a tenth of the submissions are about DH itself – not great, not terrible. We still seem to do a lot of talking about ourselves, and I’d like to see that number decrease over the next few years. Pedagogy-related submissions are also still a bit lower than I’d like, hovering around 10%. Submissions on the “World Wide Web” are decreasing, which is to be expected, and TEI isn’t far behind.

All in all, I don’t really see the trend toward “Global Digital Humanities” that the conference is themed to push, but perhaps a more complex content analysis will reveal a more global DH than we’ve sen in the past. The self-written Keyword tags (as opposed to the Topic tags, not a controlled vocabulary) reveal a bit more internationalization, although I’ll leave that analysis for a future post.

It’s worth pointing out there’s a statistical property at play that makes it difficult to see deviations from the norm. Shakespeare appears prominently because many still write about him, but even if Shakespearean research is outnumbered by work on more international playwrights, it’d be difficult to catch, because I have no category for “international playwright” – each one would be siphoned off into its own category. Thus, even if the less well-known long tail topics  significantly outweigh the more popular topics, that fact would be tough to catch.

All in all, it looks like DH2015 will be an interesting continuation of the DH tradition. Perhaps the most surprising aspect of my analysis was that nothing in it surprised me; half-way around the globe, and the trends over there are pretty identical to those in Europe and the Americas. It’ll take some more searching to see if this is a function of the submitting authors being the same as previous years (whether they’re all simply from the Western world), or whether it is actually indicative of a fairly homogeneous global digital humanities.

Stay-tuned for Part 2, where I compare the analysis to previous years’ submissions, and maybe even divine future DH conference trends using tea leaves or goat entrails or predictive modeling (whichever seems the most convincing; jury’s still out).

Notes:

  1. As far as I can tell – I used all the text similarity methods I could think of to unify the nearly-duplicate names.