According to Google Scholar, David Blei’s first topic modeling paper has received 3,540 citations since 2003. Everybody’s talking about topic models. Seriously, I’m afraid of visiting my parents this Hanukkah and hearing them ask “Scott… what’s this topic modeling I keep hearing all about?” They’re powerful, widely applicable, easy to use, and difficult to understand — a dangerous combination.

Since shortly after Blei’s first publication, researchers have been looking into the interplay between networks and topic models. This post will be about that interplay, looking at how they’ve been combined, what sorts of research those combinations can drive, and a few pitfalls to watch out for.

I thought, why not map the places that had Wikipedia articles associated with them, to see what patterns emerged.  The results of this excursion are presented below.

DBpedia is the ongoing attempt to transform Wikipedia into a semantically rich and queryable database of human knowledge.  It stores much of the categorical information found in Wikipedia articles using RDF triples–simple links for every snippet of data, from the death date of a famous (and sometimes even real) person to the season number of every Simpsons episode, to the latitude and longitude of over half a million articles on a wide variety of subjects.

Jon is the director of the Bill Lane Center for the American West, and he brought with him two undergraduate research assistants,  Jenny Rempel

“Like Morozov and Lanier, I find a similar Delusion, though one more academically minded, let’s call it the “DH Delusion.” The DH Delusion begins with a similar sort of cyber-utopianism. I remember the excitement of my first Digital History course in which it seemed not only possible, but probable that in a matter of years most scholarship would be produced in the digital medium. The Internet seemed to be promote the sort of intellectual freedom and scholastic democracy that could topple an oppressive and outdated structure of academia.”

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In sum, there’s a whole lot of new in the Digital Humanities, including what I think is already an extremely sophisticated intellectual move to cut through stale assumptions about old disciplinary boundaries, approaches to evidence, understandings of authorship, and more. The bits and bytes of the critical theory that Gibbs calls for is already happening, in my opinion, on numerous Twitter feeds, countless blogs, and at various conferences and un-conferences.

But even as we find ourselves experiencing the new, it’s just as worthwhile to locate Digital Humanities in relation to the old. For there is a return, a circling back, to pursue if we so choose. DH takes us back—in deeply illuminating ways—to age-old issues in various fields across the arts and sciences.

I have problems with the idea of infrastructure, particularly that of the e-research variety. It seems like we always end up talking about huge amounts of money and multi-institutional partnerships. It just doesn’t seem like a great model for innovation. As I’ve previously argued, I’d like to see something more like the funding schemes offered by the NEH Office for Digital Humanities. Encourage people with ideas, don’t just reward the good networkers. Build tools and apis, not portals and platforms.

Of course I’d still like to see the digital humanities well represented in the list of Virtual Laboratories and eResearch Tools currently under consideration by NeCTAR. It’s time the digital research needs of the humanities were properly recognised.