Visualization and rhetoric: Key concerns for utilizing big data in humanities research: A case study of vaccination discourses: 1918-1919

Kathleen Kerr, Bernice L. Hausman, Samah Gad, Waqas Javen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Visualization of data mining results is the linchpin of successful research in the humanities that uses computational techniques. This paper describes efforts to utilize 'big data' in a case study of news reporting on vaccination before, during, and after the 1918 influenza pandemic, focusing primarily on the conventions underlying methods of data extraction, data visualization practices, and the rhetorical impact of visualization design choices on researchers' observations and interpretive decisions. Purposeful attention to visualization and the methodological conventions that are embedded in particular visualization practices will allow humanists to have more confidence in their interpretations of big data, a key element in the acceptance of data mining as a valuable method for humanities research.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013
Pages25-32
Number of pages8
DOIs
Publication statusPublished - Dec 1 2013
Event2013 IEEE International Conference on Big Data, Big Data 2013 - Santa Clara, CA, United States
Duration: Oct 6 2013Oct 9 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013

Conference

Conference2013 IEEE International Conference on Big Data, Big Data 2013
CountryUnited States
CitySanta Clara, CA
Period10/6/1310/9/13

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All Science Journal Classification (ASJC) codes

  • Software

Cite this

Kerr, K., Hausman, B. L., Gad, S., & Javen, W. (2013). Visualization and rhetoric: Key concerns for utilizing big data in humanities research: A case study of vaccination discourses: 1918-1919. In Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013 (pp. 25-32). [6691666] (Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013). https://doi.org/10.1109/BigData.2013.6691666