HPCwire: The Next Big Thing in Humanities, Arts and Social Science Computing: Cultural Analytics

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July 29, 2008

The Next Big Thing in Humanities, Arts and Social Science Computing: Cultural Analytics

by Kevin D. Franklin and Karen Rodriguez'G, ICHASS

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In this series of articles, Kevin D. Franklin and Karen Rodriguez'G examine computational tools and approaches at the interface of humanities, arts and social science.

Cultural Analytics

Hypertext. Hypermedia. High Performance Computing. It's enough to make a humanities scholar hyperventilate. A debate has raged in the last decade (at least) about whether or not the Digital Age will see the death of The Book, The Library and perhaps, The Humanities more broadly. Part of the debate resides in the historical separation that began with Erasmus and the Renaissance, where "hard" was divorced from the "soft" sciences and arts -- a division that is still visible both geographically and intellectually on university campuses, as well as amongst scholarly disciplines themselves. But some see the reciprocal and perhaps limitless possibilities of emergent technologies and humanities scholarship -- how digital technology cuts across disciplines, creates new ways of looking at artifacts, as well as producing new forms itself.
Lev Manovich in front of HIperWall

Lev Manovich, Professor of Visual Arts at UCSD, and Director of the Software Studies Initiative at California Institute of Telecommunications and Information Technology (Calit2), is well versed in the revolutionary possibilities that lie at the intersection of the arts, humanities, social science and digital technologies. Author of Info-Aesthetics (in progress), Soft Cinema: Navigating the Database (2005), Black Box-White Cube (2005), The Language of New Media (2001), over 90 articles published in 30 countries, and a prolific lecturer on digital culture, Manovich's own professional evolution presents a narrative on breaking down disciplinary divides. Born in Moscow, he received his M.A. in Cognitive Science in 1988 from NYU, and his Ph.D. in Visual and Cultural Studies from the University of Rochester in 1993. In his most recent project, Manovich asks: How do we create quantitative measures of cultural innovation? Can we visualize cultural flows and how cultural trends change over time? Here, Manovich speaks to these (and other) questions:

What is meant by/do you mean by "Big Humanities"?

Manovich: "Big Humanities" (the term I coined in 2007) is one of the ways I use to characterize a new approach for the study of culture made possible by a convergence of a number of forces. Other terms that can be also used are "Cultural Datamining," "Culture as Data," or (my preferred term) "Cultural Analytics."

Today sciences, business, governments and other agencies rely on computer-based analysis and visualization of large data sets and data flows. They employ statistical data analysis, data mining, information visualization, scientific visualization, visual analytics, and simulation. We believe that it is time that we start applying these techniques to cultural data. The large data sets are already here, the result of the digitization efforts by museums, libraries, and companies over the last ten years (think of book scanning by Google and Amazon) and the explosive growth of newly available cultural content on the Web. (For instance, as of February 2008, Flickr had 1.2 billion images, together with tags created by users and other metadata automatically logged by Flickr servers.)

The envisioned highly-detailed interactive visualizations of cultural flows, patterns, and relationships will be based on the analysis of sets of data comparable in size to the largest data sets used in sciences. The data sets will come from a number of sources. The first source is media content -- games / visual design / music / videos / photos / art / photos of architecture, space design / blogs / Web pages, etc. In visualizing this content, we should use not only already existing metadata (such as image tags created by the users) but also new metadata that we will generate by analyzing the media content (for instance, using computer vision techniques to detect various image features). The second source is digital traces left when people discuss, create, publish, consume, share, edit, and remix these media. The third source is various Web sites that provide statistics about cultural preferences, popularity, and cultural consumption in different areas. Yet another source is what we can call "meta channels" -- blogs which track the most interesting developments in various cultural areas.

My idea of cultural analytics is related to the NEH Digital Humanities Initiative recently announced "Humanities High-Performance Computing" (HHPC) initiative, but there are some important differences. First, I am interested in analyzing and visualizing patterns not only in past culture (the traditional domain of humanities), but in contemporary cultural areas, which so far have been largely ignored by humanities -- user-generated media, portfolios by design students from around the world, and recently emerged cultural fields such as motion graphics, Web design, and space design. Second, while people have already been using statistical analysis on texts, I plan to focus on visual media -- art images, design, films, videos, computer games, Web sites. Third, building on the exciting work in visualization done today both by scientists and by artists and designers, I want to use this work as an interface for computational analysis.

In this respect, while existing cultural visualizations typically present a single graph and are hard-wired to the particular data they show, our goal is to construct an open cultural analytics research environment that will allow the user to work with different kinds of data and media all shown together: original cultural objects/conversations, cultural patterns over space and time, statistical results, etc. A user should be able to perform analysis of the data herself close to or in real time using visualization as a starting point (like in GIS). The data sets can be assembled beforehand, or harvested from the Web in real time. Ideally, such an environment will be general enough so a user would be able to connect new cultural databases and also to add new visualization/analysis modules.

How did you get involved in this area or research?

Manovich: When I was 18, I realized that my life would be driven by two passions: 1) making art (by this time I was already studying painting for six years); and 2) trying to understand how art and culture work, how we communicate visually, what are the patterns in works of art.

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