Wednesday, October 30, 2019

Using Palladio and Gephi as Data Visualization Tools

Much has been published about data visualization tools.  Miriam Posner has written in this area which I often use as a reference.   Some have even commented on the variations and differences of Gephi and Palladio

Over the last year, I've been using Palladio to examine datasets of the Chinese headtax project, which makes it easy to create bivariate network graphs to illustrate relationships between two dimensions. By default, Palladio creates a force-directed layout, which is different from Gephi.   Palladio, at the same time, is only limited to this layout. The platform has no way of doing computational or algorithmic analysis of your graphs; you will need a more powerful program like Gephi to do that work.  The most powerful method for creating networks come from programming languages such as R, Python, and Javascript. These languages allow you to control various algorithmic and aesthetic aspects of network visualizations.  Any dimension of the data can be used as the source and target of a graph.

Regardless, I still find that knowing a bit of each of the data visualization tools would be helpful for any researcher, in any phase of their research process and lifecycle.   The following video tutorials is what helps me keep myself informed about not only how to use the tools, but also weighing the strengths and weaknesses of a particular approach to playing around with the data.  I'd be interested in hearing how you approach your data.  How do you learn the tools of your trade and then decide which would be the best for your own analyses? 

Thursday, October 10, 2019

Was Shakespeare Really Shakespeare? "Shakespeare has now fully entered the era of Big Data."

Is Shakespeare really Shakespeare?  This is a question I pose whenever I'm asked about what is digital humanities.  In Shakespeare Beyond Doubt: Evidence, Argument, Controversy, two chapters are devoted to application of stylometry to Shakespeare's works and goes into much detail.   "Authorship and the evidence of stylometrics" by MacDonald Jackson and "What does textual evidence reveal about the author?" by James Mardock and Eric Rasmussen discuss an interesting aspect of these studies is that computer models using different algorithms come to similar conclusions as scholars from the "analog" era.

In 2013, The New Oxford Shakespeare made ripples in the literary world credited Christopher Marlowe as a co-author of Shakespeare’s “Henry VI,” Parts 1, 2, and 3.  Now, I've along with many throughout our literary studies have been told that there's an inevitable Marlowe-Shakespeare connection, but it isn't until more recently that scholars using distant reading techniques have used computer-aided analysis of linguistic patterns across databases to further this argument, and as Gary Taylor proposes that "Shakespeare has now fully entered the era of Big Data."   Daniel Pellock-Pelzner points out that writing a play in the sixteenth century was a bit like writing a screenplay today, with many hands revising a company’s product.   The difference is that scholars from the New Oxford Shakespeare reduces the long-held hypothesis since the Victorian era that algorithms can truly tease out the work of individual hands. 

I'm really fascinated to continue exploring this facet of literary studies, and I'm just at the beginning of my own journey.  I'm currently working on data in the sense of using R programming (which is also used in stylometry) to study the early Chinese migrants coming to Canada, and studying the data to discern patterns of migration and kinship networks.   Certainly, dipping into the literary and the historical analysis is very much in the spirit of DH.