What can we learn from the discourse around text tools? More than might be expected. The development of text analysis tools has been a feature of computing in the humanities since IBM supported Father Busa’s production of the Index Thomisticus (Tasman 1957). Despite the importance of tools in the digital humanities (DH), few have looked at the discourse around tool development to understand how the research agenda changed over the years. Recognizing the need for such an investigation a corpus of articles from the entire run of Computers and the Humanities (CHum) was analyzed using both distant and close reading techniques. By analyzing this corpus using traditional category assignments alongside topic modelling and statistical analysis we are able to gain insight into how the digital humanities shaped itself and grew as a discipline in what can be considered its “middle years,” from when the field professionalized (through the development of journals like CHum) to when it changed its name to “digital humanities.” The initial results (Simpson et al. 2013a; Simpson et al. 2013b), are at once informative and surprising, showing evidence of the maturation of the discipline and hinting at moments of change in editorial policy and the rise of the Internet as a new forum for delivering tools and information about them.
I just discovered that IBM to close Many Eyes. This is a pity. It was great environment that let people upload data and visualize it in different ways. I blogged about it ages ago (in computer ages anyway.) In particular I liked their Word Tree which seems one of the best ways to explore language use.
It seems that some of the programmers moved on and that IBM is now focusing on Watson Analytics.
Shakespeare’s ability to distil human nature into an elegant turn of phrase is rightly exalted – much remains vivid four centuries after his death. Less scrutiny has been given to statistics about the playwright and his works, which tell a story in their own right. Here we analyse the numbers behind the Bard.
The authors offer a series of visualizations of statistics about Shakespeare that are rather more of a tease than anything really interesting. They also ignore the long history of using quantitative methods to study Shakespeare going back to Mendenhall’s study of authorship using word lengths.
Mendenhall, T. C. (1901). “A Mechanical Solution of a Literary Problem.” The Popular Science Monthly. LX(7): 97-105.
I finally got around to reading the latest Pamphlets of the Stanford Literary Lab. This pamphlet, 12. Literature Measured (PDF) written by Franco Moretti, is a reflection on the Lab’s research practices and why they chose to publish pamphlets. It is apparently the introduction to a French edition of the pamphlets. The pamphlet makes some important points about their work and the digital humanities in general.
Images come first, in our pamphlets, because – by visualizing empirical findings – they constitute the specific object of study of computational criticism; they are our “text”; the counterpart to what a well-defined excerpt is to close reading. (p. 3)
I take this to mean that the image shows the empirical findings or the model drawn from the data. That model is studied through the visualization. The visualization is not an illustration or supplement.
By frustrating our expectations, failed experiments “estrange” our natural habits of thought, offering us a chance to transform them. (p. 4)
The pamphlet has a good section on failure and how that is not just a rhetorical ploy, but important to research. I would add that only certain types of failure are so. There are dumb failures too. He then moves on to the question of successes in the digital humanities and ends with an interesting reflection on how the digital humanities and Marxist criticism don’t seem to have much to do with each other.
But he (Bordieu) also stands for something less obvious, and rather perplexing: the near-absence from digital humanities, and from our own work as well, of that other sociological approach that is Marxist criticism (Raymond Williams, in “A Quantitative Literary History”, being the lone exception). This disjunction – perfectly mutual, as the indiference of Marxist criticism is only shaken by its occasional salvo against digital humanities as an accessory to the corporate attack on the university – is puzzling, considering the vast social horizon which digital archives could open to historical materialism, and the critical depth which the latter could inject into the “programming imagination”. It’s a strange state of a airs; and it’s not clear what, if anything, may eventually change it. For now, let’s just acknowledge that this is how things stand; and that – for the present writer – something needs to be done. It would be nice if, one day, big data could lead us back to big questions. (p. 7)
3quarksdaily, one of my favourite sites to read just posted a very nice essay by Sanjukta Paul on Where Probability Meets Literature and Language: Markov Models for Text Analysis. The essay starts with Markov, who in the 19th century was doing linguistic analysis by hand and goes to authorship attribution by people like Fiona Tweedie (the image above is from a study she co-authored). It also explains markov models on the way.
How is this art? I suspect it is in the way he plays with repetition. Another project, Alphabetized Newspaper, takes all the words in stories on the cover of The New York Times and rearranges them in alphabetical order created a sort of sorted word list. (Click image and explore.)
He also did this with video of NBC nightly news, which produces a bizarre effect. Imagine all the very short clips of people saying “and” in a row.
I am struck by how he has humanly recreated what an algorithm could do.
Emil Johansson, a student in Gothenburg, has created a fabulous site called the LOTRProject (or Lord Of The Rings Project. The site provides different types of visualizations about Tolkien’s world (Silmarillion, Hobbit, and LOTR) from maps to family trees to character mentions (see image above).
Lately I’ve been trying Wolfram Mathematica more an more for analytics. I was introduced to Mathematica by Bill Turkel and Ian Graham who have done some impressive stuff with it. Bill Turkel has now created a open access, open content, and open source textbook Digital Research Methods with Mathematica. The text is a Mathematica notebook itself so, if you have Mathematica you can actually use the text to do analytics on the spot.