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Nathan Dooner

Languages and Literature, De Montfort University

Thesis title:

Authorship Attribution in Early Modern Drama: A survey of the Shakespeare Apocrypha

My interest is in stylometry — the science of measuring writing styles — and it how can be best applied to the diverse field of Early Modern English. 

My research examines the Shakespeare apocrypha, a group of 17th Century plays published under Shakespeare’s name, but which have historically been regarded as containing little-to-none of his writing. By developing a new suite of stylometry software alongside my studies, my hope is to glean new insight into the authorship of these texts, while creating a set of tools that can be easily circulated to other researchers, or students, who are interested in studying authorship.

Research Area

  • Languages and Literature

Publications

"Reading Robert Greene: Recovering Shakespeare’s Rival", Shakespeare, 2023

https://www.tandfonline.com/doi/full/10.1080/17450918.2023.2226119


"Principal Component Analysis and Authorship", Digital Scholarship in the Humanities, 2023

https://academic.oup.com/dsh/advance-article-abstract/doi/10.1093/llc/fqad054/7295827


"The Old Law Table and Arden of Feversham", Notes and Queries, 2024

https://academic.oup.com/nq/advance-article/doi/10.1093/notesj/gjae106/7758213


"The problem with new claims that Marlowe’s Doctor Faustus was co-written by a forgotten dramatist", The Conversation, 2024

https://theconversation.com/the-problem-with-new-claims-that-marlowes-doctor-faustus-was-co-written-by-a-forgotten-dramatist-239968












Conferences

Irish Renaissance Society, Belfast - 2019

Quantitative Methods for Literary and Historical Scholarship, Leeds - 2022

Quantitative Methods for Literary and Historical Scholarship, Leicester - 2022

M4C Research Festival - 2023

British Shakespeare Association, Leicester - 2024

Malone Society, Oxford - 2024



Other Research Interests

Always happy to chat about applying anything in the fields of Data Science, Machine Learning, or Statistical Learning, to the humanities!