3 Key Digital Humanities Trends -- How DH Registers Changes in the Humanities Today

3 Key Digital Humanities Trends

How DH Registers Changes in the Humanities Today

Digital Humanities &

Humanities Public Engagement

IT Platforms & Tools

--e.g., http://humanistica.ualberta.ca/

-- http://pkp.sfu.ca/ojs/demo/present/index.php/demojournal/issue/current

--e.g., http://mediacommons.futureofthebook.org/mcpress/plannedobsolescence/

-- http://omeka.org/

-- http://www.wix.com/

-- http://entry.tapor.ca/

-- http://seasr.org/documentation/example-flows/text-summarizer/

Content Management Systems

(and blog engines):

• WordPress, Drupal, Joomla

New Publication Systems:

• Open Journal Systems (OJS)
• CommentPress

Multimedia Exhibition & Presentation Platforms:

• Omeka
• Wix
• YouTube, Podcasts

Data Mining & Pattern-Recognition Systems:

• "Reading Tools" in OJS
• Text-mining tools (e.g., TAPoR, SEASR)
• Google Ngram Viewer

Data & Text Visualization Systems:

• Many Eyes
• Tableau Public

Social Networking & Network Analysis Systems:

• Facebook (and Google+)
• Twitter
• Petition Systems
• Social network analysis applied to the humanities

Hacker Platforms:

• FloodNet

-- http://books.google.com/ngrams

-- http://www-958.ibm.com/software/data/cognos/manyeyes/

-- http://www.tableausoftware.com/products/public

--e.g., http://www.change.org/petitions

--e.g., http://litlab.stanford.edu/LiteraryLabPamphlet3.pdf

-- http://socialarchive.iath.virginia.edu/xtf/view?mode=RGraph&docId=bush-vannevar-1890-1974-cr.xml

-- http://rose.english.ucsb.edu/

-- http://www.thing.net/~rdom/ecd/ZapTact.html

?

Journal published on Open Journal Systems platform

(with "Reading Tools")

Social Networks & Spatialization

Natalie Henry Riche

(Microsoft Research)

Peter S. Bearman and Katherine Stovel,

"Becoming Nazi: A Model for Narrative Networks," Poetics 27.2-3 (2000)

Eric Steiner & Zephyr Frank, "The Socio-Spatial Network of Memórias Pósthumas de Brás Cubas -- Tracking Interactions Between People, Spaces, and Themes" (Stanford Spatial History Project)

Roberto Franzosi,

Quantitative Narrative Analysis (Sage, 2010)

http://www.stanford.edu/group/spatialhistory/Visualizations/memorias/memorias.html

RoSE (Research-oriented Social Environment)

http://rose.english.ucsb.edu

Quantification & Big Data

"Hard" word fields

"Abstract" word fields

[W]hat is the meaning of changes in word usage frequencies? What do we do with such data? With much current research drawing on word frequencies and other quantifiable aspects of culture, these are big questions. We can see now that the greatest challenge of developing digital humanities methods may not be how to cull data from humanistic objects, but how to analyze that data in meaningfully interpretable ways (2-3).

The general methodological problem of the digital humanities can be bluntly stated: How do we get from numbers to meaning? The objects being tracked, the evidence collected, the ways they’re analyzed—all of these are quantitative. How to move from this kind of evidence and object to qualitative arguments and insights about humanistic subjects--culture, literature, art, etc.--is not clear (46).

"Visualization of one million manga pages organized by visual characteristics challenges our normal ideas about style."

--Cultural Analytics Project,

Software Studies Program, UC San Diego

"Totemic Operator"

--Claude Lévi-Strauss, The Savage Mind

N. Katherine Hayles, How We Think: Digital Media and Contemporary Technogenesis (2012)

The controversies around "reading" suggest it is a pivotal term because its various uses are undergirded by different philosophical commitments. At one end of the spectrum, "reading" in the Traditional Humanities connotes sophisticated interpretations achieved through long years of scholarly study and immersion in primary texts. At the other end, "reading" implies a model that backgrounds human interpretation in favor of algorithms employing a minimum of assumptions about what results will prove interesting or important. . . . The further one goes along the spectrum that ends with "machine reading," the more one implicitly accepts the belief that large-scale multicausal events are caused by confluences that include a multitude of forces interacting simultaneously, many of which are nonhuman. . . . If events occur at a magnitude far exceeding individual actors and far surpassing the ability of humans to absorb the relevant information, however, "machine reading" might be a first pass toward making visible patterns that human reading could then interpret (29).

Making of the HTOED

Historical Thesaurus

of the OED

Science, Tech, Society (STS) studies

approach to the relation between

humans, machines, and systems?

Heuser and Le-Khac:

The spectra allowed us to see the trends through units understandable and familiar to us as readers and literary scholars, the actual novels, genres, and authors in our corpus. Instead of trying to make sense of term frequency behaviors of semantic fields, a rather abstract object, the spectra let us ask more grounded questions of the data: What kinds of novels correspond to the prevalence of one field over the other? Can we understand these trends in novelistic language more directly as changes in the kinds of novels being written? We see this process of translating data into meaningful forms as a key tactic in digital humanities work. This process follows the same kind of dialogic movement that we have pursued throughout our methods; turning to the novels helps us interpret the data in terms meaningful to literary history, while turning to the data helps us see literary history in new ways. (31)