Special Issue - Theorytellings: Epistemic Narratives in the Digital Humanities

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Journal of Cultural Analytics

Vol. 7, Issue 4, 2022
Theorytellings: Epistemic Narratives in the Digital Humanities

"This special issue deals with existing theory narratives and conceptions in DH scholarship. Introducing the neologism “theorytellings”, this special issue invites DH scholars to narrate and discuss their own theoretical contributions to the field."

 

Kleymann, Rabea, et al. “Foreword to the Special Issue ‘Theorytellings: Epistemic Narratives in the Digital Humanities.’” Journal of Cultural Analytics, vol. 7, no. 4, Nov. 2022, doi:10.22148/001c.55593.

 

Includes:

Abstract

The status of theory in the Digital Humanities (DH) has been the subject of much debate. As a result, we find different theory narratives competing and entangled with each other. If at all, these narratives can only be grasped and examined from a somewhat detached perspective. Here, we attempt to investigate these elusive narratives by means of a conceptual history approach. In doing so, we define different theory dimensions, ranging from specific cultural and literary theory frameworks to more generic uses of the concept of theory. We examine the use and semantic changes of these theory notions in a large corpus of DH journals. Using a mixture of heuristic methods and approaches from the field of distributional semantics, we aim to create tellable conceptual stories of DH theory.

Kleymann, Rabea, et al. “Conceptual Forays: A Corpus-Based Study of ‘Theory’ in Digital Humanities Journals.” Journal of Cultural Analytics, vol. 7, no. 4, Dec. 2022, doi:10.22148/001c.55507.

 INDIVIDUAL

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 INTERPERSONAL    :     SCIENCES               

HUMANISTIC --------------------------------------  MECHANISTIC   

SOCIOLOGY  :   POLITICAL 

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GROUP

	'theorytellings'
storytelling

Abstract

Instead of looking for new paradigms for Digital Humanities (DH), we present Grounded Theory Methodology (GTM) as a methodological approach to frame digital research practices more reflectively. By turning to the epistemological and practical implications of digital tools like Topic Modeling and digital data sources like YouTube comments, we highlight the theoretical assumptions that are already in the game—and call for more explicitness and methodical monitoring. To explain the procedures of GTM and the proposed worth for DH, we present an example of a qualitative research project using machine learning techniques to narrow down a large scale of data to human interpretable resample. The methodically monitored resampling process provided valuable means to validly minimize the amount of data without losing a qualitative trajectory of the process itself. Defining and tracing relevant content in our original data set enabled us to find related comments and textual conversations to be analyzed further. We discuss the example iteration in two ways: Our prototype and procedure show on the one hand, how qualitative research and computational methods can be better intertwined without compromising their epistemological foundations. On the other hand, we argue for an understanding of DH as research practice, that should follow an abductive research agenda in order to ground its theories in data.

Bischof, Andreas, and Konstantin Freybe. “Grounding Theory in Digital Data: A Methodological Approach for a Reflective Procedural Framework.” Journal of Cultural Analytics, vol. 7, no. 4, Dec. 2022, doi:10.22148/001c.57197.