Canons, Close Reading, and the Evolution of Method by MATTHEW WILKENS
“We need to do less close reading and more of anything and everything else that might help us extract information from and about texts as indicators of larger cultural issues. That includes bibliometrics and book historical work, data mining and quantitative text analysis, economic study of the book trade and of other cultural industries, geospatial analysis, and so on.” – Wilkens
“We may very well still need to read some of the texts closely, but text-mining methods allow us to direct our scarce attention to those materials in which we already have reason to believe we will find relevant information. Though we’re not used to framing our work in terms of rapid hypothesis testing and feature extraction, the process isn’t radically different from what we already do on a much smaller scale. Speed and scalability are major benefits of this strand of computational work.” – Wilkens
How does this fit in with art? Slow art day Art appreciation about getting people to slow down, slow looking.
Trending: The Promises and the Challenges of Big Social Data by Lev Manovich
“But this gap will eventually disappear when humanists start working with born-digital user-generated content (such as billions of photos on Flickr), users online communication (comments about photos), user created metadata (tags) and transaction data (when and from there the photos were uploaded). This web content and data are infinitely larger than all already digitized cultural heritage, and, in contrast to the fixed number of historical artifacts, is growing constantly. (I expect that the number of photos uploaded to Facebook daily is larger than all artifacts stored in all world’s musems.)” – Manovich
Why would art historians be interested in this data?
“In the 20th century, the study of the social and the cultural relied on two types of data: “surface data” about lots of people and “deep data” about the few individuals or small groups. The first approach was used in all disciplines that adapted quantitative methods (i.e., statistical, mathematical or computational techniques for analyzing data. The relevant fields include quantitative schools of sociology, economics, political science, communication studies, and marketing research. The second approach was typical of humanities: literary studies, art history, film studies, history. It was also used in non-quantitative schools in psychology (for instance, psychoanalysis and Gestalt psychology), sociology (Wilhelm Dilthey, Max Weber,3/17 Georg Simmel), anthropology, and ethnography. The examples of relevant methods are hermeneutics, participant observation, thick description, semiotics, and close reading.” – Manovich
- art in the age of big data – http://nicolatriscott.org/2012/09/23/art-in-the-age-of-big-data/
- http://blogs.getty.edu/iris/getty-voices-rethinking-art-history/ – one of the questions they discussed – “How can “big data”—the compiling and analysis of vast data-sets to perform statistical analysis and other calculations—be harnessed to serve art history?”
Explicit Art Historical Image Referencing on a Big Scale by Martin Warnke
Last chapter on big data and provides possible uses including – “By analyzing the pictorial references in the web and the activities of the users, it will be possible to find out what the varied meanings of an image are, dynamically changing from person to person, from time to time, from place to place, or in its broadest sense, what the collective meaning given by a vast number of users to a particular object is.” – Warnke