Folgert Karsdorp is a tenure-track researcher at the Meertens Institute of the Royal Netherlands Academy of Arts and Sciences. His research is interdisciplinary, adopting computational methods to study the field of humanities, in particular folkloristics. Karsdorp’s research interests lie in the development of computational text analysis methods in the context of ethnology, literary theory and cultural evolution. Below you will find an overview of his academic publications, current projects and academic teaching endeavors.
PhD in Computational Folkloristics, 2016
RMA (MPhil) Linguistics, 2009
BA Dutch Language and Culture, 2007
Manseeks is a really simple concordancing application without any whistles let alone bells. It shines in its simplicity, speed and cross-platform availability. Please take the app for a spin, and, in case you find any problems or have suggestion on how to improve the app, file an issue at https://github.com/fbkarsdorp/manseeks/issues.
The proposed research is aimed at providing a cultural evolutionary account of how song and music traditions live and die. What makes some of them stick, thrive, or go extinct? Finding answers to these questions serves to further our understanding of the fundamental and more general question of how to explain prevalent culture, in which some cultural artifacts are more likely to survive than others (see e.g., Sperber 1996).
With the recent rise of computational approaches to analysing culture, large audiences of younger scholars in the Humanities and Social Sciences are confronted with the need to understand and integrate computational methodologies in their scholarly work. However, they often lack the theoretical insights to evaluate which methodologies are needed for their purposes, or they lack the technical skills to tackle such an undertaking. Together with colleagues like Mike Kestemont and Allen Riddell, I attempt to remedy this situation by developing course materials, workshops, trainings and tutorials on programming and data analysis for the humanities and allied social sciences.
To what extent can neural network text generation systems be of aid to professional writers? In the Asibot project, we collaborated with award-winning author Ronald Giphart to find out.
The hypothesis of this project is that tunes and tales both consist of motif sequences acting as vehicles of oral transmission. First, a computer program will be developed for the automatic recognition of motifs in large amounts of data. Secondly, generative models will be created to simulate oral transmission including the inherent variation. This model will be able to predict the occurrence of variable motif patterns in oral tradition over time.