Pirating Texts

Project Summary

In tracing the publication history, geographical spread, and content of “pirated” copies of Daniel Defoe’s Robinson Crusoe, Gabriel Guedes (Math, Global Cultural Studies), Lucian Li (Computer Science, History), and Orgil Batzaya (Math, Computer Science) explored the complications of looking at a data set that saw drastic changes over the last three centuries in terms of spelling and grammar, which offered new challenges to data cleanup. By asking questions of the effectiveness of “distant reading” techniques for comparing thousands of different editions of Robinson Crusoe, the students learned how to think about the appropriateness of myriad computational methods like doc2vec and topic modeling. Through these methods, the students started to ask, at what point does one start seeing patterns that were invisible at a human scale of reading (reading one book at a time)? While the project did not definitively answer these questions, it did provide paths for further inquiry.

The team published their results at: https://orgilbatzaya.github.io/pirating-texts-site/

Click here for the Executive Summary

Themes and Categories
Year
2018
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu

Disciplines Involved: English, Literature, History, Geography, Visual & Media Studies

Project Lead: Charlotte Sussman

Project Manager: Grant Glass

This project aimed at further exploring how to better develop different methods for doing humanities based research by combining the open-ended nature of humanities projects with the methodological rigor of fields like statistics and computer science. Lucuan Li noticed the potential for finding new ways to link these methods to the humanities: “The open-endedness gave us tremendous freedom to determine our modes of analysis and which parts of the data we would use.” Orgil Batzaya found drawing links between data insights and historical facts compelling: “We looked at distributions of the concentration of publication in different countries and it was fun trying to link historical periods to peaks and troughs in publication.” Some of these links became profoundly obvious according to Gabe Guedes: “As for the final outcome, I was surprised to be able to see such a strong correlation between historical events and publication volume, to the point where you had very noticeable peaks when countries made substantial imperial forays.”

The team was directed and mentored by Grant Glass, a graduate student in the English Department at UNC-CH. Grant’s own research focuses on the question, what is a text? This project allowed Grant to begin to form the data structure for creating a new edition of Robinson Crusoe by understanding how thousands of copies are related to one another. The experience and insights took Grant by surprise: “I did not think that there was as much variance between the copies as there was. This new understanding of the text will help me describe how reading publics, publishers, and editors shape the text long after the author is gone.”

Related People

Related Projects

The Air Force’s F-15E Strike Eagle jets have parts that wear down and break, causing unscheduled maintenance events that take away valuable time in the air for critical missions and training. Our team, Limitless Data, is working with Seymour Johnson Air Force Base to mine manually entered maintenance data to visualize and predict aircraft failures. We created a prototype data visualization product that will enable maintainers on the flight line and help them identify and repair critical failures before they happen, keeping jets ready to fly, fight and win.

 

Faculty Lead: Dr. Emma Rasiel

Client Lead: Lt. Devon Burger

Project Manger:  Vignesh Kumaresan

This project aims to improve the computational efficiency of signal operations, e.g., sampling and multiplying signals. We design machine learning-based signal processing modules that use an adaptive sampling strategy and interpolation to generate a good approximation of the exact output. While ensuring a low error level, improvements in computational efficiency can be expected for digital signal processing systems using the implemented self-adjusting modules.

Project Leads: Yi Feng, Vahid Tarokh

 

Click here to view the project team's poster

 

Watch the team's final presentation (on Zoom) here:

 

Mapping History has focused on the categorizing, labelling, digitization, and 3D reconstruction of 16th & 17th century maps & atlases of London and Lisbon. Over the course of the summer, the Mapping History team has developed its own unique analytical dataset by painstakingly labelling every element contained within these maps, used python to digitize this dataset, and, now in the projects final stage, has begun the process of reconstructing these historical perspectives in a 3D game engine.

Project Lead: Philip Stern, Ed Triplett

Project Manager: Sam Horewood

 

View the team's final poster here

Watch the team's final presentation (on Zoom) below: