A Map for Big Data Research in Digital Humanities

Abstract

This article is an attempt to represent Big Data research in digital humanities as a structured research field. A division in three concentric areas of study is presented. Challenges in the first circlefocusing on the processing and interpretations of large cultural datasets -can be organized linearly following the data processing pipeline. Challenges in the second circle -concerning digital culture at large -can be structured around the different relations linking massive datasets, large communities, collective discourses, global actors, and the software medium. Challenges in the third circledealing with the experience of big data -can be described within a continuous space of possible interfaces organized around three poles: immersion, abstraction, and language. By identifying research challenges in all these domains, the article illustrates how this initial cartography could be helpful to organize the exploration of the various dimensions of Big Data Digital Humanities research.

Digital preservation at Big Data scales: proposing a step-change in preservation system architectures

“…Karpf, 2012;Edwards et al, 2013) and Digital Humanities (e.g. : Manovich, 2012;Kaplan, 2015;Terras et al, 2017) perhaps represents the greater focus of Big Data Analysis in library-related domains.…”

Conceptualizing Big Social Data

“…Their vision is based on Manovich's ideology [38], which is focused on bringing the potential of social or cultural data into humanities and social sciences. Thus, Jean Burgess and Axel Bruns present the BSD concept by mentioning the shift of Big Data towards media, communication, cultural and computational social science, which has led to the wave of research on digital humanities [39][40][41]. According to Burgess and Bruns, such changes "...provoked in large part by the dramatic quantitative growth and apparently increased cultural importance of social mediahence, "big social data".…”

Big Data-Driven Health Surveillance

“…Bronson and Knezevic 2016;Lazer et al 2014;Oboler, Welsh, and Cruz 2012) or big data-driven research in universities and non-profit institutions (see e.g. Borgman 2015;Gold and Klein 2016;Kaplan 2015;Franke et al 2016, Wyatt et al 2013, Kitchin 2013. For example, due to the dominance of media corporations in retrieving usergenerated big data, research institutions are increasingly dependent on access conditions defined by these companies.…”