Website: Principal Investigator(s): Prof. David Kim, Germanic
WorldLiterature@UCLA originates in the desire to document translational networks of literature in the intersection of conventional humanistic methods and digital technologies. Its conceptual origin lies in Goethe’s widely read conversation with Eckermann about the Chinese novel and world literature in 1827, but it builds thick information networks around this historical event with reference to contemporary scholarship on Orientalism, world literature, and translation. A digital humanities exploration of world literature for the twenty-first century, it brings together the content and the methodology of various disciplines to experiment with a rigorously interdisciplinary approach to the study of culture, literature, and language in global modernity.
Illustrative of this ambitious goal, WorldLiterature@UCLA adapts social network theory, in general, and an open source interactive network platform, in particular, to move between disciplinary scholarships and multimodal practices of knowledge production. Close reading and distant reading, verbal expression and visual information processing, subject-dependent interpretation and fact-based data collection converge in this digital project, as users engage in multilayered and complex relational representations of a literary work across time and space. Here, network graphs serve as visual representations of what they document in collaboration and the accompanying textual descriptions specify further questions and claims that are inscribed in those visualizations. Instead of embodying an accurate, fixed, and empirical archive of relations, WorldLiterature@UCLA seeks to combine faculty research with student learning in a collaborative, dynamic, and iterative manner.
HumTech is contributing experts in network visualization to train the students in Prof. Kim’s Winter 2017 German 170 course, and programming expertise to develop a set of WordPress plugins that will make it easier for David’s students to visualize their data sets in the future.