DH09: 2 Presentations: Automatic Conjecture Generation in the Digital Humanities; Co-word Analysis of Research Topics in the Digital Humanities

1) 'Automatic Conjecture Generation in the Digital Humanities' by Patrick Juola and Ashley Bernola. 'Computers are useless because they can only give you answers' (Picasso). 'I believe that we can use computers to generate questions.' Computer should perform light analysis as well as data organization, farm out 'routine' reading to computer, and intervene only when somethin 'unordinary' occurs. Structure of Graffitti has: a list of graph invariants(max degree, girth, # of nodes...), template-based conjectures, program generates random graph, and if conjecture passes many tests, publish for mathematicians to prove. Epistemological background: graffiti generates ideas. Program called 'the Conjecturator': apply graffiti-like structure to generation of text analytic conjectures; generate random testable hypothesis, etc. Components include template, thesaurus, database and metadata, statistics, prototype, explanations of conjecture outputs, etc. See: www.twitter.com/conjecturator for examples of conjecture that mostly turn out to be untrue and/or uninteresting. What makes "interesting" conjectures? (i.e. conjectures worth working on?). How can this be improved? The conjecturator can generate evidence and a reading list for further research.


2)'Co-word Analysis of Research Topics in the Digital Humanities' by Xiaoguang Wang and Mitsuyuki Inaba. How do we define 'digital humanities?' Get a clear view of the structure and evolution of digital humanities research based on metholodgy of scientometorics. Use a second order study for cognizing research questions.

A variety of methods were discussed. One method : co-word analyisis explores conceptual networks in various disciplines, such as scientometorics. Used the coefficient index; data collection from 600 articles from DH journals, ALLC, ACH, LLC, DHQ. Listed top 36 high frequency keywords, temporal change of term frequency. For example, contrasting the use of digital humanities and humanities computing, one see the increase of use in the former and a decrease use in the latter. Using a cluster map, one can see humanities computing moving from the center of the map as a large node in each passing year to a peripheral, smaller mode in 2008. Futher discussion: full text analysis vs keyword analysis, combining keywords vs splitting keywords, synonymous term vs different term, and inductive approach vs deductive approach. Conclusion: enhanced understanding of direction development of the term 'digital humanities,' no clear subdisciplines in digital humanities, and digital humanities can be considered a gateway for emerging research topics.