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Machine learning can offer new tools, fresh insights for the humanities

Enlarge / Composite image based on Jacques-Louis David’s unfinished painting, “Drawing of the Tennis Court Oath” (circa 1790). (credit: Association of Cybernetic Historians)

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.

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