Between Text, Argument, and Data: Interpreting New Visualizations in History

Monday, January 5, 2015: 12:00 PM
Murray Hill Suite A (New York Hilton)
Fred Gibbs, University of New Mexico
As various text mining techniques such as topic modeling and network analysis become more widespread in humanities research, the visualizations that help make sense of complex computational analytics play an increasingly significant role in our analyses and interpretations of the historical record. Although other disciplines are now well accustomed to using data visualizations, graphical techniques present new challenges for historians who want to represent not only data, but also the uncertainty and ambiguity that characterizes interpretive work in the humanities. Although historians recognize visualizations as symbolic representations rather than as realistic depictions of data, these visualizations are inevitably embedded with accidental signifiers, making arguments that their authors do not necessarily intend. Such a danger only intensifies as datasets, tools, and visualizations themselves become increasingly complex and routinely generated through automated and often only partially configurable processes. It becomes difficult to distinguish features of a visualization that arose from deliberate design choices and convey useful information from those which are simply arbitrary artifacts of automation. Grounded in the kinds of visualizations that now literally and figuratively represent cutting-edge work in the Digital Humanities, this paper critically examines the intersection of text mining, visualization, and design in the service of historical interpretation and scholarly communication. Some guiding questions: Does the quality of visualizations implicitly reflect the quality of scholarship? Should authors be required to explain their design choices? Should the aesthetics of visualizations be held to the same standard as other forms of scholarly work? How do visualizations foster new relationships between text, argument, and data? How are visualizations produced by historians implicitly read as those produced in other disciplines, even those with different methodological and epistemological underpinnings?
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