Today marks the release of the Journal of Digital Humanities 2.1, focused on topic modeling and with myself and Scott Weingart as guest editors. It is an excellent collection of material about topic models and topic modeling and their application to humanities research. It even includes a section for community responses to be collected later and topic modeled and added to the issue in a month’s time.
Before I get into what I think is most interesting about a publication like this, I want to thank Matt Jockers, who, while noticeably absent from this issue, with the release of Macroanalysis will have more than enough publication this month to make up for it. I have a feeling Matt’s book will become required reading for a host of new DH scholars. Matt introduced me to topic modeling nearly three years ago, explained how it worked, taught me how to pronounce “Dirichlet”, and most importantly made clear that you couldn’t simply accept the results of the model without understanding how it worked and how it could fail. I also need to thank Glen Worthey, our digital humanities librarian here at Stanford, for helping me to realize what it was I was trying to get at in the intro to the issue by explaining to me the concept of a “synechdoche”, which is embedded in the claim Scott and I make about topic modeling and its relationship to this endeavor we clumsily refer to as digital humanities.
Its being an example of the promise and problems of digital humanities research is what I find so interesting about topic modeling. While I’ve used the technique to support digital humanities research by modeling free text content in databases or traditional corpora or Wikipedia entries, I’ve always been more intrigued by the model itself. My piece on Comprehending the Digital Humanities, written over two years ago, was not about trying to use text analysis to understand digital humanities scholars and their self-definition so much as it was about demonstrating explicitly and visually that taking part in the use of such a computational method was something that defines the digital humanities.
It’s the troubled relationship with computational methods such as topic modeling that makes digital humanities so valuable. As is already stated in this issue of JDH–and demonstrated throughout–it is precisely the digital humanities scholars using computational methods that are so critical of such computational methods. Their critiques are far more substantial, and subtle, than those put forth by traditional humanities scholars that don’t understand how these tools and methods really work. But that wariness drives more sophisticated and more interesting application of computational methods in humanities scholarship, as I think is also demonstrated in this issue.
I want to thank everyone at JDH, especially Joan Troyano, who made this such a rewarding experience.