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Oral History auf dem Weg zu Big Data: menschliche und maschinelle Annotation lebensgeschichtlicher Interviews im Vergleich

Authors: Egger, Nils; Franken, Lina; Möbus, Dennis; Schmid, Florian;

Oral History auf dem Weg zu Big Data: menschliche und maschinelle Annotation lebensgeschichtlicher Interviews im Vergleich

Abstract

Auch Oral History Daten sind als Big Data vorhanden und damit mit automatisierten Methoden analysierbar. Der Beitrag untersucht, wie qualitative Annotation und Topic Modeling als Machine Learning Verfahren ineinander greifen können. Dafür erarbeiteten in einem Workshop 16 Teilnehmende Vergleichsperspektiven durch die beiden Zugänge ins Material. Im Ergebnis ergänzen sich beide Perspektiven: So können mit Hilfe der Grounded Theory - insbesondere im diskursiven Austausch innerhalb einer Gruppe - Sinnstiftungsprozesse minutiös und akkurat herausgearbeitet werden. Das Topic Modeling hingegen spielt seine Stärken in der rasanten Verschlagwortung ganzer Interviewkorpora aus. Auf der anderen Seite ist das Arbeiten nach den Regeln der Grounded Theory äußerst zeitintensiv und konsistente Topics können Sinnstiftung suggerieren, wo letztlich reine Statistik am Werk ist. Ein Beitrag zur 9. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" - DHd 2023 Open Humanities Open Culture.

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Keywords

Theoretisierung, Interviewtranskripte, Topic Modeling, Annotieren, Grounded Theory, DHd2023, Text

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