to narrative expression (storytelling) in social media.

Conclusion

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. 25 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.

acknowledgments

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 ( [e-mail]) is research professor in the Center for discrete Mathematics and theoretical Computer science of rutgers university, Piscataway, nJ. Peter M. Broadwell ( [e-mail]) 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 ( [e-mail]) 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.