Most of my research questions boil down to the same thing: how did information move through space, and what happened to it on the way?
That question is hard to answer one document at a time. So I’ve built datasets — of citations, advertisements, subscriptions, mottoes — and used topic modeling, text mining, mapping, and network analysis to see patterns no amount of close reading would surface.
Citationswho cited whom
Who cited whom tells you how information flowed through the revolutionary Atlantic. My book explains why.




More at citation maps. This work was discussed in Sharon Block’s “#DigEarlyAm: Reflections on Digital Humanities and Early American Studies,” William and Mary Quarterly (October 2019).
Projects
Published
Newspaper-Brokered Slave Trade Advertisements
The quantitative database behind “Enquire of the Printer,” published in the Journal of Slavery and Data Preservation. A StoryMaps companion gives the same research a visual reading.
Ongoing
Recovering Haitian Revolutionary Print Culture
Using U.S. newspaper reprints to reconstruct an archive of papers published in St. Domingue during the Haitian Revolution &mdash. Assembling a lost print record from its copies. Still in progress, long stalled.
Dataset
Eighteenth-century newspaper mottoes
Collected by decade. Produced an essay on mastheads and the Revolution for the Journal of the American Revolution.
Tutorialsfree to assign
Step-by-step exercises written for undergraduates with no technical background. Both use openly available hitorical data, so students work with real material.
2020
Making simple network graphs with Palladio
Students build the citation network above from the underlying data.
2020
Topic modeling the State of the Union with MALLET
Adapted from The Programming Historian, with a prepared corpus.
Adapting any of this for your own students? Tell me how it goes.