Machine learning can offer new tools, fresh insights for the humanities | Gaming & Culture - Ars Technica

From the French Revolution to the history of the novel, Big Data makes its mark, as Ars Technica reports. 

Photo: Composite image based on Jacques-Louis David's unfinished painting, "Drawing of the Tennis Court Oath" (circa 1790).

Truly revolutionary political transformations are naturally of great interest to historians, and the French Revolution at the end of the 18th century is widely regarded as one of the most influential, serving as a model for building other European democracies. A paper published last summer in the Proceedings of the National Academy of Sciences, offers new insight into how the members of the first National Constituent Assembly hammered out the details of this new type of governance.

Specifically, rhetorical innovations by key influential figures (like Robespierre) played a critical role in persuading others to accept what were, at the time, audacious principles of governance, according to co-author Simon DeDeo, a former physicist who now applies mathematical techniques to the study of historical and current cultural phenomena. And the cutting-edge machine learning methods he developed to reach that conclusion are now being employed by other scholars of history and literature.

It's part of the rise of so-called "digital humanities." As more and more archives are digitized, scholars are applying various analytical tools to those rich datasets, such as Google N-gram, Bookworm, and WordNet. Tagged and searchable archives mean connecting the dots between different records is much easier. Close reading of selected sources—the traditional method of historians—gives a deep but narrow view. Quantitative computational analysis has the potential to combine that kind of close reading with a broader, more generalized bird's-eye approach that might reveal hidden patterns or trends that otherwise might have escaped notice.

"It's like any other tool and can be used for good or bad; it depends on how you use it," said co-author Rebecca Spang, a historian at Indiana University Bloomington. "Crucially, one thing this so-called 'distant reading' can do is help us identify new questions and things we could not have recognized as questions reading in the slow, close way that human individuals read." Small wonder that an increasing number of historians is applying these kinds of digital tools to the growing number of digitized archives. Stanford University historian Caroline Winterer, for instance, has used the digitized letters of Benjamin Franklin to map his "social network," revealing a picture of his rise to global prominence that was previously hidden...

Ted Underwood, a literature professor at the University of Illinois, is using DeDeo's tools to analyze the text of 40,000 novels spanning two centuries. Underwood originally specialized in British Romantic literature, focusing on individual authors and books. But he now focuses on longer time scales "because that's the scale where I think we know the least," he said.

DeDeo's method is particularly suited for that kind of analysis. They met at one of Piper's McGill 
workshops, where DeDeo spoke on using text mining to study the novel. "I'm on record as saying the talk made me want to run immediately out of the room and try and apply it to lit history to see what we can learn," said Underwood.
Read more...

Source: Ars Technica