This is my brief literature of relevant sources for my dissertation, including scholars from digital art history, data visualization, social network analysis, and cultural analysis.
In the field of digital art history, Johanna Drucker’s article is a very influential piece and reflects the ongoing struggle of digital humanities to convince art historians and renew rigid traditional methodologies. Drucker urges that there is a need to demonstrate that “digital methods change the way we understand the objects of our inquiry” (2013, p.6). She predicts that new aspects of artworks can be discovered by introducing computational, statistical and informational analysis, which “will provide new bases on which the judgment of the trained historian can build” (p.6). As an example of work in digital art history, Drucker mentions the work of Criminisi, Kemp and Zisserman (2005) on the analysis of construction of perspectival space in paintings and defines it as the basis of visual data mining. Furthermore, Drucker clearly separates digitized art history from digital art history and states that the latter is developing slowly due to the challenge that the primarily visual – as opposed to textual – object of art history pose to technology. The author emphasizes that “digitization is not representation but interpretation” (p.12) and sees great potential in this recognition for critical insight. She refers to the work of Lev Manovich in the field of cultural analytics, designed to create tools and methods for analysing large amounts of images, using parameters to sort visual information (p.8).
Mannovich postulates that people “in art history need to learn the core concepts that underlie the use of data science in contemporary societies” (2015, p.15). He states that in order to create representations of data, three crucial decisions have to be made: What are the boundaries of the phenomenon? What are the objects we will represent? What characteristics of each object will be included? (pp.15–16).
Visualizing these networks of social connections has to be approached thoughtfully as Porras points out that “Network visualizations have the potential to translate messy archival work into clouds of connection, maps of relations that can reveal hidden agents or nodes of production” (2017, p.42). Porras draws on Galloway’s Zuhandenheit problem “where digital tools are used unconsciously and without critical reflection” (Porras 2017, p.44) and urges that it is necessary to identify ways in which power differentials translate into the production of data (p.44). Galloway warns that “as the digital humanities expands, the ideological infrastructure will become more emboldened” (Berry and Galloway 2016, p.162), a point which is taken very seriously by Porras.
As early as 1974, Becker’s “general proposition that knowledge and cultural product are social in character or have a social base” (1974, p.767) formed the basis for social network analysis in the field of art history. He explains that conventions are what makes the production and reception of artworks possible, and conventions are created through social interaction and negotiation. In order to understand a specific art work, Becker suggests to “think of social organization as a network of people who cooperate to produce that work” (1974, p.774). Based on Becker’s work, Di Maggio writes about cultural networks and the ways in which they can be used to understand society.
In the field of Social Network Analysis, DiMaggio (2011) talks about art as cultural products, that are produced in certain settings and mentions four approaches that have recently been dominating network analysis of creative fields. These are “Bourdieu’s theory of competition; theories of efficient boundaries; research on small worlds; and analyses of structural mechanisms that induce creative or financial success” (p.288). Di Maggio refers to Collins’ (1989, 1998, 2000) claims about networks that drive intellectual movements, that there is a tendency of dense connections between eminent thinkers and well-known peers, which can lead to “reputational contagion” (p.289) and movement leaders creating new ties to extend their influence outside of their schools.
Becker, H.S. 1974. Art as Collective Action. American Sociological Review, 39(6), pp.767–776.
Berry, D.M. and Galloway, A.R. 2016. A Network is a Network is a Network: Reflections on the Computational and the Societies of Control. Theory, Culture & Society, 33(4), pp.151–172.
Collins, R. 1998. The sociology of philosophies: a global theory of intellectual change. Cambridge, Mass: Belknap Press of Harvard University Press.
Collins, R. 2000. The Sociology of Philosophies: A Précis. Philosophy of the Social Sciences, 30(2), pp.157–201.
Collins, R. 1989. Toward a Theory of Intellectual Change: The Social Causes of Philosophies. Science, Technology, & Human Values, 14(2), pp.107–140.
Criminisi, A., Kemp, M. and Zisserman, A. 2005. Bringing Pictorical Spaces to Life: Computer Techniques for the Analysis of Paintings. IN: Digital art history: A Subject in Transition. Computers and the history of art (Yearbook). Bristol: Intellect.
DiMaggio, P. 2011. Chapter 20: Cultural Networks. IN: J. Scott and P. J. Carrington (eds.) The SAGE Handbook of Social Network Analysis. London; Thousand Oaks, Calif: SAGE, pp. 286–300.
Drucker, J. 2013. Is There a “Digital” Art History? Visual Resources, 29(1–2), pp.5–13.
Manovich, L. 2015. Data Science and Digital Art History. International Journal for Digital Art History, 1(1), pp.13–35.
Porras, S. 2017. Keeping Our Eyes Open: Visualizing Networks and Art History. Artl@s Bulletin, 6(3), pp.42–49.