“The Venus Hottentot (1925)” as a network

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At MSA13 this week, I will be presenting a couple of ways I have started mapping ekphrasis using social network analysis. The following visualization is a very early working through of how to identify “nodes” in the poem and how to define their relationships. In this case, I have “named” the subjectivities, voices, locations, languages, and “actors” within the poem. Then, in an Excel spreadsheet, I placed any subject initiating an action (defined as describing, narrating, relating, comparing, envoicing, placing, observing, etc) in the first column and the correlating object of that action in the second column. In other words, this formalizes my understanding that ekphrasis is something done to something else by someone else for someone else. The following is only a very preliminary visualization using the Network Diagram tool in Many Eyes.

 

 

Mapping a network by hand

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As I’ve mentioned elsewhere, mapping networks by hand is a predictable way of beginning to define how one might go about determining “nodes” and “edges.”  I’ve been using a presentation tool called Prezi to begin playing with what the “nodes” could be in an ekphrastic poem and doing so has also made me think very specifically about what I mean by “exchange” as a way of defining the relationships between those nodes. Here is one of my initial hand drawings of the relationships between various voices, subjects, artworks, and locations in Elizabeth Alexander’s “The Venus Hottentot (1025).

Reading failure

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Today’s reading demonstrates how social network analysis can be used to read the silences, the absences, and the failures of text to fill in detail.  Lauren Klein’s article at ARCADE titled “When Reading Fails” today points to the difficulty of reading James Hemmings, Thomas Jefferson’s cook and Sally Hemmings’ older brother.  By mining the Jefferson papers, digitized at the University of Virginia, she visualizes Jefferson’s correspondence and learns that you can begin to see a story about Jefferson’s relationship to his staff by understanding the frequency and subjects about which he corresponded with them.  What I find particularly interesting is her method of visualization.  Rather than the usual “hairball” that typifies most SNA visualizations, this one uses a single line to track correspondence.

Why networks?

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While putting together my logic for why social networks make good models for ekphrasis, I find that it’s important to always keep the instabilities of that decision close at hand.  Working with networks in the humanities seems promising, but there’s also good reason for skepticism… a point that I think Elijah Meeks articulates quite well in his recent post “Hacking Networks in the Humanities.”  I just wish I could go to the THATCamp where he introduces Gephi.

Mining the Humanities

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Yesterday’s MITH Ditigal Dialogues series brought Aditi Muralidharan to campus to give a talk titled “Large Scale Text Analysis in the Digital Humanities: Methods and Challenges.”  Aditi’s talk addressed, among a variety of issues, the challenges facing humanists and computer scientists who want to pursue types of research like textual analytics.  A substantive portion of the conversation after Aditi’s talk returned to the complexities of working across disciplines.  One thing seemed clear from the discussion: For a large-scale text mining project to work, there must be something mutually beneficial about it for all scholars involved.  In other words, it must push the computer scientist in ways that are recognized and deemed significant in his or her field as well as render for the humanist something that adds to our existing knowledge of the texts.  This seems to me to be one of the fundamental concepts of understanding what work is in the digital humanities.  It must be a win-win-win to work (humanist, computer scientist, granting agency).