to narrative expression (storytelling) in
In rural Denmark in the 19th century,
stories would reflect the trouble farmers had with everything from ghosts
to house elves. The trouble with house
elves begins with their unpredictable
nature, which, for folklorists, makes
them difficult to track through the landscape of the story corpus. This exploration of the contours of computational
folkloristics and our description of preliminary experiments in multimodal
network classification for folklore corpora offer not only the possibility of being able to track house elves but promising directions for future work.
One troubling aspect of working
with a large amount of humanities
data is that scholars often cannot see
the forest for the trees, to borrow a
folk expression. Moretti spoke convincingly of distant reading, a corrective to the long-standing tradition in
the humanities of very close reading.
Distant reading allows scholars to “see
the forest,” discovering patterns that
might otherwise be obscured by too
close attention to the detail of a text or
performance; the same can be said of
the methods outlined here.
Fortunately, with these computational methods, researchers are able
to combine distant reading with close
reading. In so doing, they can interrogate the relationship between folk
expressive culture and the individuals
who created and perpetuated these expressions in time and place. Computational folkloristics offers an opportunity to read and interpret culture in a
more holistic fashion than ever before.
We wish to thank Nischal Devanur for
help in processing the data. We also
thank colleagues at the Institute for
Pure and Applied Mathematics (UCLA),
participants in Rice University’s “
Technology, Cognition, and Culture” lecture series, and Indiana University’s
“Networks and Complex Systems”
symposium for comments and suggestions on earlier versions of this work.
The research was funded by an American Council of Learned Societies Digital Innovation Fellowship and National
Science Foundation grant IIS-0970179.
Many of the ideas were developed at the
National Endowment for the Humanities Institute for Advanced Topics in
Digital Humanities through “Networks
and Network Analysis for the Humanities” NEH grant HT5001609. The work
of James Abello is partially supported
by the Center for Discrete Mathematics and Theoretical Computer Science
(DIMACS), Rutgers University, Piscataway, NJ, “Special Focus on Algorithmic Foundations of the Internet” NSF
grant #CNS-0721113, and mgvis.com
( http://mgvis.com), New Jersey.
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James Abello ( firstname.lastname@example.org) is research
professor in the Center for discrete Mathematics and
theoretical Computer science of rutgers university,
Peter M. Broadwell ( email@example.com)
is a Council on library and information resources
postdoctoral fellow in the digital initiatives department
of the Charles e. young research library at the university
of California, los angeles.
Timothy R. Tangherlini ( firstname.lastname@example.org) is
a professor of folklore in the scandinavian section and
the department of asian languages and Cultures at the
university of California, los angeles and a fellow of the
american Folklore society.