Social Network Visualization: Charting the Associations of Hegemony

Social network analysis is a research method for investigating the relationships between actors within a social context. This method of analysis can be used to examine any network of social relationships and exchanges. It can investigate patterns of gossip within a high school or citation patterns across an academic discipline. What is central to social network analysis is not the content of the relations and exchanges but rather the process of identifying and representing the structure of those relations and exchanges.

Within social network analysis, the analytic activity and visual form are reciprocal. The method produces a visualization that orders and depicts the relations between chosen social actors in the chosen context, and it does so in a manner that allows those relationships and various permutations of them to be documented and explored. Social network visualizations are produced by codifying the relationship between actors in the system, marking their connections, and often assigning weights (representative numeric values) to those relationships to denote the strength of the connections between any two or more actors. Various algorithms and software packages can be used to process these relations and produce a depiction of actors arranged in space such that their spatial distribution from other actors and at times the visual quality of the connection to other actors is meaningful to the relationship status under investigation.9 From the resulting image, a researcher might be able to make claims about strong or weak ties among members, expose connections among individuals or groups that might otherwise have gone unnoticed, or distinguish individuals or groups for specific interventions to disrupt or bolster the strength of the network. Social network analysis and the commensurate visualizations thus lend themselves to the tactic of revealing hegemony because it provides a method and form attuned to charting the associations and flow of resources among people, organizations, and practices.

They Rule (2001, 2004, 2011)10 and Exxon Secrets (2004)11 are two projects by Josh On that employ social network analysis and visualization techniques to represent the structures and patterns of influence across corporations and other institutions. They Rule (figure 2.2) is an online interactive social network visualization that allows users to explore a dataset containing the names of Fortune 100 companies and their board members. Specifically, with this visualization, users are able to produce images that depict the cross-affiliations among board members of Fortune 100 companies. A user may begin constructing the visualization by either company, institution, or person. If a company or institution is selected it appears on the screen and the user can select to view the board members of that institution or company, which appear arrayed around the image of a board table. Clicking on board members then draws lines connecting to all of the other boards they sit on. The user can continue the exploration process with each member. As the image of the network is constructed, the appearance of the board member is manipulated: the more boards that board members sit on, the fatter they grow. Users can also choose to begin with a single person, and then explore the connections between that person and the boards they sit on. In addition to self-directed exploration of the dataset, users can automate the process of finding connections between companies or institutions. By selecting Find Connection from the menu, a user can

Figure 2.2

Josh On, They Rule (2001, 2004, 2011), Map created by user ssackz, on the 2011 version of They Rule. This map depicts the relationship between the oil and media industries and is titled Oily Media.

select any two companies or institutions to be automatically searched, and the software produces a visualization that shows how those two companies are connected via their board members. As yet another mode of interaction, users can save maps they have created, thereby contributing to an ongoing archive of documented structures and patterns of connection between corporate boards.

The social network visualization Exxon Secrets (figure 2.3) is built from the same software code base as They Rule but is focused on a different domain. Sponsored by the environmental advocacy organization Greenpeace (USA), Exxon Secrets is a social network application that exposes and documents the influence of the petroleum company Exxon-Mobil in shaping climate-change debate, regulation, and legislation. The visualizations chart the funding that the Exxon Mobil Foundation provides to researchers, lobbyists, and organizations that are climate-change skeptics and who question the reality or significance of climate change. The implication of this visualization is that the Exxon Mobil Foundation uses networks of funding to influence the climate-change debate by supporting research and public relations efforts that counter claims that climate change is a detrimental phenomenon that is in part caused or exacerbated by fossil fuel production and use. The purpose of the Exxon Secrets

Figure 2.3

Josh On, Exxon Secrets (2004),

visualization, then, is to present the complex lattices of financial relations that shape this discourse and action.

The actors in Exxon Secrets are scientists, spokespeople, and organizations and are categorized by funding amounts. Instead of They Rule's icons of boardrooms and businessmen and -women, Exxon Secrets uses icons of government buildings (overlaid with dollar signs that change in size) and icons of heads (differentiated by gender and role). As with They Rule, users can construct network representations either by organization or individual. The user then can show all of the organizations that an individual is connected to or all of the individuals connected to an organization. For each individual or organization, a user can view a panel of detailed information, including background information, notable quotes and deeds, and a timeline of funding received from the Exxon Mobil Foundation. In addition, as with They Rule, Exxon Secrets includes a set of premade visualizations.

As examples of agonistic computational information design, both They Rule and Exxon Secrets chart associations among institutions, individuals, and issues, with the implication that these relations form a structure through which influence is exerted. In Exxon Secrets, these relations are complex and involve myriad actors and events that intersect with the subject of climate change. The inclusion of events and organizational details in Exxon Secrets is a significant addition to the functionality of They Rule. It extends the capacity of the software to reveal hegemony because it allows users to discover patterns of action as they develop and change over time in response to specific issues. By showing how actors, resources, and interests align in variable configurations that are not reducible to simplistic or obvious patterns, the visualization expresses the dynamic and contingent nature of hegemony. For example, with Exxon Secrets, users can view and compare the networks of associations formed around "Climate Stewardship Act Attack" with those of the "Global Warming Legislation Attack." With these two visualizations, the arrangements of individuals and institutions across the events can be studied to develop an understanding of the political landscape of climate change generally and around each of these events specifically.

By providing views into the actors and forces involved in a particular issue, Exxon Secrets works to represent hegemony as a conglomeration of ideas and intentions from a diversity of sources. Hegemony, so depicted, is not a structure or condition based on class distinction. Instead, it is a condition of associations and attachments to an issue—in this case, to work against climate-change research and associated legislation. This notion of associations and attachments, more so than dividing lines along the class, status, or even political party affiliation, characterizes the contemporary understanding of hegemony. Because of their formal qualities, social network visualizations are particularly suited to provide representations of these connections and orderings of resources to issues. Moreover, by providing simple capacities for interactivity with the visualization, these projects also suggest the potential to use computational media to produce ever more dynamic views into the construction and exertion of hegemony in contemporary society.

Nearly a decade after the first instantiation of They Rule, social network analysis and visualizations continue to be used by artists and designers to examine hegemony. However, with changes in the context and capabilities of computational media have come changes in the processes and products of computational information design and its agonistic variants. Creating They Rule and Exxon Secrets required that On search, locate, and organize the data that underlie the representations, but today such data are more readily available, formatted, and able to be accessed online. An increasing number of tools also automate or at least proceduralize the visual formatting and display of data. From this confluence of factors comes the mashup—a distinctive practice and form with significance to computational information design.

The mashup as form derives from music and is akin to the remix: it is a combinatorial form made from the blending of preexisting parts.12 In computational information design, a mashup is data (of any kind) that come from two or more sources and are brought together to produce a new form or functionality. In most domains of computational media, the term mashup refers to a Web-based software application that draws on data and presentation layers from two or more digital applications or services to produce a third, distinctive, digital application or service. Mashups often rely on application programming interfaces (APIs), which are sets of access points and exchange techniques that allow a programmer to write software that accesses data from one application or service and passes it to another. The central activity of creating a mashup is using code to suture together data into a new form, and it is made possible by the malleability and interoperability of digital data—the qualities of computational media that enable transcoding.

The visualization State-Machine: Agency, discussed at the start of this chapter, is one example of computational information design via the mashup: it draws from a series of online databases and other resources to acquire the data that it combines and then expresses. Another example is Skye Bender-deMoll and Greg Michalec 's the Unfluence project (2007) 13 (figure 2.4). Like They Rule and Exxon Secrets, the Unfluence project uses a social network visualization and computational information design to reveal patterns of influence across institutions, organizations, and individuals. Like State-Machine: Agency, those networks and forces of influence concern campaign contributions. In fact, both State-Machine: Agency and Unfluence were winners in the Sunlight Foundation's14 2007 mashup contest that sought innovative examples of the use of computational media to promote government transparency. What distinguishes the Unfluence project from State-Machine: Agency, They Rule, and Exxon Secrets is the extent to which it draws from other sources and the capabilities it provides for exploration. As an artifact of computational information design, the Unfluence project exemplifies a procedural approach to information design and highlights issues regarding the role of the image in evoking the political.

Arriving at the Web page for the Unfluence project, a user specifies the state, year, government office (governor, state senate, state house, state assembly, state supreme court), category of people from whom the contributions come (political action constituents and lobbyists, such as accountants, health professionals, conservative Christians, or pro-life activists),

Figure 2.4

Skye Bender-deMoll and Greg Michalec, Unfluence (2007), http://unfluence.primate. net

and a dollar amount (such as greater than $500, greater than $1,000, or greater than $10,000). The user then clicks on Generate Graph, and a social network visualization is produced that represents all the contributions to all the candidates within the specified election and contribution size. Users can then explore the visualization. Circles representing donors are green, their size is relative to donors' total contributions. Arrows emanating from the donor circles mark their connections with the candidates. The circles of candidates are red or blue, depending on their political party affiliation, and their size represents the total amount of money received from all contributors. Pointing at any circle (that is, any candidate or donor) displays the name of that individual or organization. Clicking on the circle opens an auxiliary window that searches for and retrieves information (if present) regarding the individual' s voting record or past contributions.

Bender-deMoll and Michalec (2007) describe the process of generating the Unfluence visualization by detailing what this design process involves:

A query is generated from your search settings and sent to National Institute on Money in State Politics ' API which looks in their databases and returns a list of matching candidates as an xml file. For each candidate we get a list of the top contributors, and discard any with contributions below the value threshold you set. This donor-recipient information is formatted into a network and passed to a program called GraphViz that computes positions for the nodes and draws it (with help from ImageMagick). The image is passed back to you. When you click on a node, we send queries to NIMSP and Project VoteSmart to check if there is information available (this requires some hacked scraping and matching code) for that candidate, and include the links in the info bubble. The visual effects are provided by

This description conveys the ways in which computational information design is increasingly a practice of procedurally acquiring, referencing, and combining data. The artifact that results from this process—the image itself—does not exist a priori and is not set in its final appearance by the designer. Instead, the image emerges from and is procedurally expressive of the data and the code written to render that data. That is, the form that the designer provides is not the final form of the image but rather the rules from which to construct the image. This mode of producing representations is characteristic of computational artifacts and systems and marks a point of distinction from other media forms. As Bogost (2007, 4) describes, "To write procedurally, one authors code that enforce rules to generate some kind of representation, rather than authoring the representation itself." In the case of Unfluence, even the formal qualities of the image of the network—its so-called look and feel—are the direct effect of the software libraries used.

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