Network Analysis of Literary Translation in the U.S.

Presentation with Prof. Thomas Beebee, Liberal Arts Scholarship and Technology Summit, Penn State University, September 2014.

Transcript

1. Network Analysis of Literary Translation in the U.S. Thomas Beebee & Dawn Childress
 LASTS 2014
3. Fig. 2 from Moretti’s Network Theory, Plot Analysis
4. Fig. 51 from Moretti’s Network Theory, Plot Analysis
6. Mike Bostock’s Matrix Representation of Character Co-Occurence in Les Misérables
7. Translation Networks Dataset • Three Percent’s Translation Database (U of Rochester Comp Lit) • Book-length works translated into English in U.S. market • 2008-2014 • Title, author, translator, publisher, genre, original language, country, price Next: prep & clean the data!
8. Nodes & Edges
9. A web of authors, translators, & publishers
10. Translators & publishers of German language texts, 2008-2014
11. Method Community detection • Based on Modularity, or the total number of links within detected communities compared with total number of links pointing out of detected communities • 22 detected communities Betweenness Centrality (of Node) • Based on Connected Components, in this case the number of shortest paths between which a node sits
12. Translators & publishers of German language texts, 2008-2014
13. Next steps • Adding new facets to the data set (gender, affiliation, publisher type, geo data, author involvement) • Compare results: How do translation communities of different languages compare? • Expand data to include works translated into other languages/ markets
14. Final thoughts