CANCELLED: Data-driven history with Jo Guldi

This event has been cancelled until further notice. If you would like to be notified of the new date as soon as it is confirmed, please add you details to the mailing list.

Data-Driven History: Text Mining the History of Property Law in the Debates of Britain's Parliament, 1806-1911

This talk offers a case-study of a multi-level, AI-driven research on a major problem in history: the story of property law in the modern world. It applies topic modeling, n-gram analysis, skip grams, phrase detection, sentiment analysis, guided vocabularies, geoparsing, and dynamic topic models to understand the changing valences of how contemporaries discussed the ownership and inhabitation of property over time. On the basis of these quantitative approaches, the project derives a new history of property, challenging conservative accounts of the history of property law that describe a set of principles unchanged since Locke, much like Newton's discovery of gravity.

Please note that refreshments will not be served, however there is a cafe within the British Library where beverages can be purchased ahead of the talk.

About the speaker

Jo Guldi is one of the foremost practitioners of digital history. In 2014, she co-authored The History Manifesto, an open-access pamphlet on using text-mining to look at history over long time periods. She is also PI of The Unaffordable World, a $1 million NSF grant to apply NLP to investigate long-term questions of property in the parliamentary debates of Great Britain. Most recently, she has authored several papers on the measurement of time, identifying discontinuities in the historical record, nesting topic models, and the principle of "Critical Search," a model of humanities-style critical thinking applied to questions of big data.

Schedule:

3pm Presentation

4pm Q&A

4.20pm Coffee/Tea & Networking

5pm End

This event is part of The Alan Turing Institute's Living With Machines project (funded by AHRC) and in collaboration with the Humanities and Data Science Turing Interest Group.

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