Rachel Stuart, a student enrolled in this semester’s ENG 5998 reading group, reflects on some of the readings provided by Professor Mundy for our upcoming discussion on Data Visualization and Graphics Scripting:
This week, FSU’s Digital Scholars group has access to a participant in the kinds of projects that engage digital data’s proliferation in society. The linkage between data, information, culture, and art is made visible in the research and works created by individuals like Professor Owen Mundy of FSU’s Department of Art. Our speaker this week is the only person I know of who has had his work covered by entities like NPR and Vice, and can simultaneously boast that his name in a Google search bar is automatically paired with the word “cat.”
As technology has changed, two factors have increased that greatly contribute to the need for thinkers like Mundy: the proliferation of data, created and collected through digital tools and resources, and the ubiquity of our online lives, bordering on oversharing. Sepandar D. Kamvar and Jonathan Harris explore the connection between these factors, considering the ways that society-at-large records emotion via publicly posted social or blog media. Their project is called We Feel Fine, and these efforts go beyond creating an artistic representation of emotion as it exists online.
The tool that resulted is an emotional search engine, what Kamvar and Harris call “Experiential Data Visualization” and provides “immersive item-level interaction with data” (1). Ultimately, We Feel Fine operates with an interface that invites users to play with data, to learn from universal experience, and to think about their own emotions within the context of this larger data sampling of emotion. It is simultaneously instructive and fun, which might be linked back to what it is doing to begin with; this tool takes data (objective and measurable numbers of emotional mentions) and translates it into art (far more subjective and interactive, even hypothetical).
There is also a divide between the source material (data) and the end result of their efforts (the work) in their mobility; Kamvar and Harris even call the different approaches that a user can take to the information “movements.” The data does truly move, swirling and growing, trembling and falling as the user delineates how they want to experience the data. This “animation of data” relates back to a point made by Mitchell Whitelaw of the University of Canberra, in his article “Art Against Information: Case Studies in Data Practice.” According to Whitelaw, data becomes information when it is granted contextualization and organization – some might argue, when it is granted meaning. This “transubstantiation” of sorts collapses the gap between the data set and the data referent. In a beautiful moment of linguistic serendipity, the animation (from Latin animus, animi, “mind, soul, life force”) of data by Kamvar and Harris takes us beyond the numbers of individuals feeling anger or sympathy or ennui and connects us back to the soul of the individual behind the numbers.
What we don’t recognize is that while data appears to be lifeless, objective, and harmless, the streams of data that occur online carry information useful to many individuals besides artists. Professor Mundy points to the accessibility of personal data in his “I Know Where Your Cat Lives” project, where images of cats are linked to the geographical information available when the person who posts the image uploads it. We create data. We create online trails of our lives that are trackable and mappable and, in contrast to the social media records we curate, often are an accurate history of our lives both online and off. Mundy’s map of cats makes it clear how we lack privacy online, no matter how we may try to erase traces of our true selves.
In a sense then, while these digital data projects often incorporate art as a means of communicating the informative aspect of data, there is an attempt to avoid artifice in the data communicated. In fact, Kamvar and Harris considered how to map emotions without granting positive or negative associates via the tool. They were careful not to rate these emotions, and built an interface that would give the same approaches to anger as it would to joy or embarrassment. In order to differentiate, however, they did color code the emotions. (This does, in my opinion imbue them with some kind of status. A bubble that is a sunny yellow is obviously preferable to one that is a muddy puce. Perhaps that’s just me?)
Kamvar, Sepandar D. and Jonathan Harris. “We Feel Fine and Searching the Emotional Web.” Web Search and Data Mining 2011. Hong Kong, China. 9-12 Feb. 2011.
Whitelaw, Mitchell. “Art Against Information: Case Studies in Data Practice.” The Fiberculture Journal 11 (2008): n. pag. Web. 16 May 2015.
Willis, Derek. “What the Internet Can See From Your Cat Pictures.” The New York Times. The New York Times, 22 July 2014. Web. 14 Mar. 2015.