Digital Humanities Projects with Small and Unusual Data: Some Experiences from the Trenches

Update March 15 2016: This content was selected for Digital Humanities Now by Editor-in-Chief Joshua Catalano based on nominations by Editors-at-Large: Ann Hanlon, Harika Kottakota, Heather Hill, Heriberto Sierra, Jill Buban, Marisha Caswell, Nuria Rodriguez Ortega. The slides have now been integrated, and they can also be seen in a reveal.js presentation here.

This is an edited version of a talk I gave at UC Irvine on February 5, at a Symposium on Data Science and Digital Humanities organized by Peter Krapp and Geoffrey Bowker.

I’ve made the focus of my talk Digital Humanities projects involving small and unusual data. What constitutes small and unusual will mean different things to different people, so keep in mind that I’ll always be speaking in relative terms. My working definition of small and unusual data will be texts and languages that are typically not used for developing and testing the tools, methods, and techniques used for Big Data analysis. I’ll be using “Big Data” as my straw man, even though most data sets in the Humanities are much smaller than those for whom the term is typically used in other fields. But I want to distinguish the types of data I will be discussing the from large corpora of hundreds or thousands of novels in Modern English which are the basis of important Digital Humanities work.… Read more…

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How to Create Topic Clouds with Lexos

Some Background

Topic modelling is gaining increasing momentum as a research method in Digital Humanities, with MALLET as the general tool of choice. However, many would-be topic modellers have struggled to make effective use of MALLET’s output, which is raw data. In fact, there has been a growing movement to devise methods of visualising topic modelling data generally. A while back, Elijah Meeks had an idea for generating topic clouds: separate word clouds for each topic in the model. [I can’t seem to access his original blog post, but here is his code on GitHub.] Although word clouds have their problems as visualisations, Meeks speculated that they were particularly effective for examining topics in a topic model. Indeed, others have used word clouds to visualise topic modelling results, most notable Matt Jockers in the digital supplement to his Macroanalysis. One of the things I liked about Meeks’ implementation using d3.js was that it placed the clouds next to each other so that they could be compared.

I quickly transferred this idea to our work on the Lexomics project, and our software Lexos. In Lexomics, we frequently cut texts into chunks or segments, which can then be clustered to measure similarities and differences.… Read more…

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Digital Humanities as Gamified Scholarship

The Digital Humanities trace their origins back to Father Roberto Busa’s efforts to analyse the works of Thomas Aquinas in the 1940s, which was then followed by further efforts to perform textual analysis with the aid of computers. Since that time, the Digital Humanities has expanded to encompass a myriad of other activities (and acquired its name in the process) and a devoted community of practitioners. Nevertheless, doubts persist about whether the growth of the Digital Humanities has had, or has the potential to have, any significant impact on scholarship in the Humanities as a whole.  Although I can’t say for certain, my feeling is that when doubters look back at the past, they tend to be thinking primarily of computational textual analysis as the method that has failed to obtain a wide impact. Whether this is a fair assessment of the Digital Humanities, or whether the appropriate criteria have been selected for assessing the significance for even this one area, is worthy of discussion, but my intention here is to look forward, rather than back. Computational textual analysis is beginning to evolve more rapidly, and to become more widely accessible to both students and scholars, meaning that the past should not be taken as an indication of the future.… Read more…

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