1.1 From Digital Humanities to Speculative Computing - UCLA ...

creation of concordances, word lists, and other statistical information about a text. This
supports stylometrics, the statistical analysis of characteristics of style for purposes of
attribution. It also allows large quantities of text to be searched for discourse analysis,
particularly that which is based on reading a word, term, or name in all its many contexts
across a corpus of texts.
Many of the questions that can be asked using these methods are well-served by
automation. Finding every instance of a word or name in a large body of work is so
tedious and repetitive that the basic grunt-work – like that performed by the
aforementioned Father Busa – takes so long that the analysis was deferred during years of
data gathering by hand. Automating highly defined tasks creates enormous amounts of
statistical data. The data then suggest other approaches to the study at hand. The use of
pronouns vs. proper names, the use of first person plural vs. singular, the reduction or
expansion of vocabulary, use of Latinate vs. Germanic forms – these are very basic
elements of linguistic analysis in textual studies that give rise to interesting speculation
and scholarly projects. xxii Seeing patterns across data is a powerful effect of aggregation.
Such basic automated searching and analysis can be performed on any text that has been
put into electronic form.
In the last decade the processes for statistical analysis have sophisticated
dramatically. String searches on ASCII (keyboarded) text have been superceded by
folksonomies and tag clouds generated automatically through tracking patterns of use. xxiii
Search engines and analytic tools no longer rely exclusively on the tedious work of
human agents as part of the computational procedure. xxiv Data mining allows context
dependent and context-independent variables to be put into play in ways that would have
1.1 History / 3/2008 /
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