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Developing a web-application for comparative historical data-mining in public media from different countries. Prof.Dr . Toine Pieters & Dr. Pim Huijnen Utrecht University p.huijnen@uu.nl Sheffield, 06/09/2012. Research project: BILAND.
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Developing a web-application for comparative historical data-mining in public media from different countries Prof.Dr. ToinePieters & Dr. PimHuijnen Utrecht University p.huijnen@uu.nl Sheffield, 06/09/2012
Research project: BILAND History of mentalities: making explicit tacit beliefs, standards, morals Casus: Concepts of genetics/eugenics in culture Development of a digital data-mining tool
Eugenics To improve the genetic quality of the human race Control over sexual reproduction Encouragement of procreation, measures of sterilization and segregation Francis Galton (1822-1911)
Research questions (1) Public discourse not monopolized by “hard-line” eugenics: What did this mean for the dissemination of genetic and eugenic thinking in Dutch public discourse? What were the political and racial connotations of the arguments that circulated in newspapers and how did they become manifest in different domains?
Research questions (2) Comparison German and Dutch public discourse in the interwar period: How, when and to what extent did discourses about heredity, genetics and eugenics in Germany began to differentiate from the Netherlands?
Research on public debates (1) Condit (1999): Analysis explicit stances on eugenics backbone study: 653 articlesfrom American periodicals 1900-1995
Research on public debates (2) Sources: Royal Library The Hague Staatsbibliothek zu Berlin
Research on public debates (2) No limitation source material No selection issues No representativeness issues Enabling research on hiddendebates, mentalities, implicitnotions
Keywords (examples) Ancestry Lineage Descent Stock Reproduction Regulation Selection pure/purity Progression Evolvement Deterioration Depravation Isolation segregation
Research on public debates (2) No limitation source material No selection issues No representativeness issues Enabling research on hiddendebates, mentalities, implicitnotions Reproducibility of research, fromvariousperspectives
How does it work? (1) keywords Queries Quantitative analysis
Quantitative analysis No. of hits Sources Word cloud – sentiments, NER Timeline
NamedEntityRecognition Geographical Proper names Institutionalnames
Timeline Query: Sports + Heredity
How does it work? (2) Lexica Queries Quantitative analysis Qualitative analysis Insight …andagain!