Digital Humanities & Data Science: A Short Reading List

Cindy Nguyen

Examiner: David Bamman

Outside Field: Digital Humanities & Data Science

Literature/Project Review

(By Topic in order of relevance to own work)

1. Computational text analysis (broadly, literature and social sciences)
   1. Read all Literary Lab pamphlets (11 pamphlets)
   2. Grimmer and Stewart, “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts” https://web.stanford.edu/~jgrimmer/tad2.pdf
   3. Monroe et al., “Fightin’ Words”
      372. http://pan.oxfordjournals.org/content/16/4/372.abstract
   4. Piper, Andrew. “Novel Devotions: Conversional Reading, Computational Modeling, and the Modern Novel.” New Literary History1 (2015).
   5. O’Connor, Brendan, David Bamman, and Noah Smith, “Computational Text Analysis for Social Science: Model Assumptions and Complexity,” NIPS Workshop on Computational Social Science, December 2011.
2. Quantitative formalism, distant reading
   1. Moretti, F. (2007) Graphs, Maps, Trees: Abstract Models for a Literary History, Verso, London
   2. Stephen Best and Sharon Marcus, “Surface Reading: An Introduction,” Representations 108, no. 1 (November 2009): 1–21.
   3. Bode, Katherine. Reading by Numbers: Recalibrating the Literary Field. Anthem Scholarship in the Digital Age. London ; New York: Anthem Press, 2012. http://site.ebrary.com/lib/berkeley/Doc?id=10595451. (shelf)
3. Network Analysis
   1. Stiller et al. (2003), “The Small World of Shakespeare’s Plays,” Human Nature
   2. So, Richard Jean, and Hoyt Long, “Network Analysis and the Sociology of Modernism,” boundary 22 (2013). “Literary Pattern Recognition: Modernism Between Close Reading and Machine Learning” (Long and So, Critical Inquiry, Winter 2016). “Fog and Steel: Mapping Communities of Literary Translation in an Information Age”(Long, Journal of Japanese Studies, Summer 2015). Color figures available here.
   3. PROJECT: http://republicofletters.stanford.edu
   4. PROJECT: http://www.sixdegreesoffrancisbacon.com
4. Topic Modeling
   1. Jockers, Matthew L. and Mimno, David. Significant Themes in 19th-Century Literature, August 2012.
   2. Meeks, Elijah, and Scott B. Weingart. “The Digital Humanities Contribution to Topic Modeling.” Journal of Digital Humanities, April 9, 2013. http://journalofdigitalhumanities.org/2-1/dh-contribution-to-topic-modeling/.
   3.  Ted Underwood
      1. “The Quiet Transformations of Literary Studies: What Thirteen Thousand Scholars Could Tell Us” (New Literary History 45, no. 3 [Summer 2014]) and accompanying website: http://rci.rutgers.edu/~ag978/quiet/#/about
      2. “Understanding Genre in a Collection of a Million Volumes, Interim Report.” Accessed February 3, 2016. https://figshare.com/articles/Understanding_Genre_in_a_Collection_of_a_Million_Volumes_Interim_Report/1281251.
5. Authorship Attribution
   1. Mosteller and Wallace (1963), “Inference in an Authorship Problem,” JASA.
   2. Holmes 1994, Authorship Attribution
6. Text Reuse
   1. “Reprinting, Circulation, and the Network Author in Antebellum Newspapers” and “Computational Methods for Uncovering Reprinted Texts in Antebellum Newspapers,” published in American Literary History 27.3 (August 2015).
   2. PROJECT: http://viraltexts.org
7. Culturonomics, Cliometrics (remove?)
   1. Morse-Gagné, Elise E. “Culturomics: Statistical Traps Muddy the Data.” Science 332, no. 6025 (April 1, 2011): 35. doi:10.1126/science.332.6025.35-b.
   2. Swierenga, Robert P. “Clio and Computers: A Survey of Computerized Research in History.” Computers and the Humanities 5, no. 1 (September 1970): 1–21. doi:10.1007/BF02404252.
   3. Introductory articles on “culturomics” from Science, “Quantitative Analysis of Culture Using Millions of Digitized Books” (2010). Available here: http://www.sciencemag.org/content/early/2010/12/15/science.1199644
8. Algorithmic thinking
   1. Burrell (2016), How the Machine ‘Thinks:’ Understanding Opacity in Machine Learning Algorithms
   2. Shmueli, Galit. “To Explain or to Predict?” Statistical Science3 (2010).
9. “Data”
   1. Masters, Christine L. “Women’s Ways of Structuring Data.” Ada: A Journal of Gender, New Media, and Technology, November 1, 2015. http://adanewmedia.org/2015/11/issue8-masters/.
   2. Owens, Trevor J. “Defining Data for Humanists: Text, Artifact, Information or Evidence?” Journal of Digital Humanities 1, no. 1 (Winter 2011).
10. Criticisms
    1. Liu, Alan. “Where Is Cultural Criticism in the Digital Humanities.” In Debates in the Digital Humanities, 2012. http://dhdebates.gc.cuny.edu/debates/text/29.
    2. Debates in the Digital Humanities, Matthew Gold, Mineesota Press, 2012
    3. Kathleen Fitzpatrick, Planned Obsolescence: Publishing, Technology, the Future of the Academy. New York: NYU Press, 2011.
    4. Wendy Chun, “The Dark Side of the Digital Humanities – Part 1”
    5. Alexis Lothian and Amanda Phillips, “Can Digital Humanities Mean Transformative Critique?” Journal of E-Media Studies. Vol. 3 Issue 1. 2013 http://journals.dartmouth.edu/cgi-bin/WebObjects/Journals.woa/1/xmlpage/4/article/425