Part II: Network Sensibilities as Generative Tool
(From network analysis to network sensibilities: Part II)
Network Sensibilities as Generative Tool
Most directly, network analysis strategies promote visual mapping of key elements (nodes), connections between them, and the overall structure of the system. This type of visualization can be used in generative ways throughout a study. By generative, I include the processes of generating data, generating organizational strategies for one’s data, generating multiple analytic coding schemes or categories, and generating links between levels such as local/global, relational/structural, and so forth. While the focus may be primarily directed toward the phenomenon, it is equally beneficial to use network sensibilities to map one’s own conceptual and epistemological standpoints.
Mapping notable moments and connections
When I consider the origin of the idea of mapping, the cartographer comes to mind. In a practical sense, one primary goal of mapping is to identify where one is situated as well as where one has been, in order to direct or guide other travelers unfamiliar with the territory. When applied to cultural rather than physical terrain, this mapping might produce a visual image of primary or notable landmarks, such as key points of intersection among cultural members, clusters or groupings such as those defined by kinship, age, gender, interests, and key moments, such as rites of passage, rituals, or significant shifts prompted by unusual breaks in patterns. In fieldwork-driven research contexts, this mapping is often produced as a supplement to textual/descriptive fieldnotes. Here, I suggest it can be used to generate many layers of what might be construed as data, each laid over previous or alternate iterations to illustrate different orientations, generate different objects for analysis, identify different patterns, and demonstrate analytical shifts over time.
Identifying the elements influencing a situation
Adele Clarke provides a compelling way to map situations visually, an analytical practice that combines elements of grounded theory, actor network theory, and traditional sociological mapping techniques. The key to this type of “situational analysis” is to use one’s field data to generate more data for analysis. The process is to generate various kinds of maps: Situational maps (figure 1) consider the major human, non-human, discursive, and other elements influencing a situation, as framed by those in the situation as well as the analyst. These maps are intended to provoke analyses of relations among them (Clarke, 2003, p. 559). Relational maps (figures 2 and 3) take each element in turn as the center of the network, considering the nature of the relationship between this element and other elements that have been specified in the situation. Although tedious, this process of shifting the networks in a meticulous way can trigger important analytical breakthroughs, particularly patterns or elements that are obscure or nonobvious to those in the situation (p. 569). Social worlds/arenas maps, “lay out all the collective actors and the arena(s) of commitment within which they engage” the situation (p. 559). Position maps (figure 4) “lay out the major positions taken and not taken, in the data vis a vis particular discursive axes of variation and difference, concern, and controversy” (p. 560).
|Figure 1. Abstract situational map to illustrate the messy process of laying out various pertinent elements of a situation (Clarke 2003, p. 562)|
|Figures 2 and 3. Relational maps centering two different situational elements and drawing the relevant network of relations. (Clarke, 2005, pp. 104 and 105, respectively)|
By creating this range of maps of the situation, the researcher focuses in serial fashion on particular elements of the situation (a person, an issue, an event, a time period, a theme, a technology or medium, etc.) or notable patterns in larger assemblages. Through this process of analysis, more complicated understandings of the situation emerge. This process cannot help but be deeply iterative with each pass through the data.
To draw a map is to lay out elements in relation, so as to find or create a pattern that is sensible for a particular purpose or audience. Setting aside the practical aspects of mapping as the process of producing a verisimilitude and simplification of the ‘landscape’ so that others can find their way without getting lost, one can begin to note the more creative aspects of mapping as a process of adding complexity to the situation, generating additional data for research. This might seem to fly in the face of the goal of narrowing one’s research scope to a sensible level, but highlights a crucial element of qualitative inquiry: seeking depth and complexity in order to reach thick description. Geertz classically described these multiple layers of meaning “winks upon winks.” Playing with different possible mappings can help pull this complexity to the surface.
Trying out different maps of the situation can help identify certain general patterns or curiosities that might not otherwise be noticed. This acknowledges the challenge that despite one’s goal of identifying a discrete object for inquiry, the object will always be entangled in larger patterns and flows of meaning that operate both at the surface, observable levels and also at less visible, deep structure levels. When adding the premise of swiftly shifting or ad hoc structures, the utility of situational network mapping becomes more meaningful.
As a large-scale example, one could look at how this might operate when trying to study social media in relation to the Japanese earthquake in early 2011. This event had monumental physical consequences and sparked a global series of overlapping and intermingled reactions. In such a huge complex situation it would be impossible to identify simple cause/effect sequences or to explain as a whole. Indeed, everyone I talk with about this crisis has a different interpretation of what happened and what it means. Quelling the urge to describe or explain the entire situation, we can begin with the baseline question of “how did people make sense of the Japanese earthquake through social media?” We can start to generate data by tracking interesting data paths and mapping various elements, beginning at the structural level, the individual level, or anywhere between. Take a massive dataset such as the number of Tweets and Retweets during the hours following the quake. Creating an animated visualization of the initial response to the disaster by individuals across the globe, as Twitter did (figure 5) gives one layer of information for further analysis.
Zooming in on particular tweets, one could draw more detailed mappings based on the content of the messages. Or one could compare the timeline of this response to similar animations of responses to other recent acute events, such as the Queensland floods of late 2010 or the Egypt protests of early 2011.
One could alternately begin at the molecular level of the message, following any unit of information as it moves and morphs (or withers and fades). Take for example a YouTube video entitled “Japan,” passed around a small network (figure 6 and 7). If the concept of building complexity is taken seriously, this soon generates multiple types of maps and possible directions for further analysis, not of the video itself but of the assemblages it helps constitute, along with other units of cultural information (e.g., figures 8 and 9).
|Figure 6 and 7. Screenshot and close-up from Youtube video entitled “Japan,” which was posted on a Facebook page shortly after the Japanese earthquake of early 2011. (Aikisystema, 2011)|
As Clarke notes, it is vital to keep this process of mapping consciously messy, to avoid premature closure (2004, p. 95). This may be seem to be just a fancy way of saying that open-ended brainstorming is an important aspect of inquiry, but it goes beyond this. The act of mapping adds complexity that will swiftly engulf the initial thing we thought we wanted to study, thereby removing emphasis from a precise object of analysis. This is identified as a strength, as it then becomes easier to focus on the research question and the data rather than pre-determined theoretical or empirical objects.
We most often encounter network maps as the final product of research that focuses on describing large-scale situations. This can be obvious, as in figure 10, where the map looks like what we commonly think of as a network map:
|Figure 10. Network map (and close up) overviewing news discourse on the events in North Africa and the Middle East during the first half of 2011. Focus on five thematic clusters in five major Swedish newspapers. Image shows initial rough analysis only. (Lindgren, 2011)|
They can be subtle or almost invisible, when they don’t look like network maps but are based on network thinking, as we see in political commentator Glenn Beck’s chalkboard drawings of the Egypt situation in one of his news programs (figure 11):
|Figure 11. Screenshot (and close-up) from online video clip of the Glenn Beck television program on Fox News, January 31, 2011. Discussion during this part of the program focuses on explanations of what influenced the riots in Egypt. Cartoon faces represent the nature of each country’s relationship with the United States. (MacNicol, 2011)|
They can be animated and seemingly comprehensive, which is increasingly the case with the rising popularity of data visualization. This timeline (Figure 12) produced by The Guardian, for example, charts major information streams throughout the Arab Spring, emphasizing times, types, and sources of information.
Figure 12. Screenshot from “The Path of Protest” interactive timeline developed by the Guardian to trace key events surrounding what has become termed “Arab Spring.” (Blight, Pulham, & Torpey, 2012)
Figure 13 illustrates another visually arresting image that strives to make an argument about influence and Tweeting during the early 2011 Egypt protests.
|Figure 13. Twitter map of pro-democracy movement in Egypt during early 2011. Image intended to illustrate freedom of expression made possible by Twitter. Red is Arabic speaking, Blue is English. Nodes are placed in proximity with those they influence, although no operational definition of ‘influence’ or explanation of methodology is provided by author (Boguta, 2011)|
All these examples represent the (somewhat) final product of a process of using network analysis to think about, analyze, and represent a phenomenon. These final images do not capture how network analysis works in actuality. We might better identify this by doing our own mapping of maps presented across multiple outlets. Each production will present a different argument about a similar phenomenon. Even a cursory glimpse of the way various stakeholders described or explained the Egypt protests shows the complexity of possibilities. Daily, if we were paying attention to the situation, we could see a wide range of visualizations, each presenting a partial depiction, as measured by innumerable variables depending on who is doing the mapping and for what purpose. While some focused on speed and diffusion of information, others focused on relative position and power of individuals and/or key stakeholders. Still others traced the geo-located origins of messages and their subsequent travel, mapping the epicenter or apparent impact of tidal waves of information. This list could go on and on. Taken together and over time, these mappings illuminate the power of thinking about situations through a network lens. They also form networks of meaning of their own, not only by virtue of a viewer’s experience of them, but also more directly when and if they influence each others’ re-renderings over time. We can see that while the focus is ostensibly on an object (social media or protests in the Arab world), the astonishing outcome is that the parts are much more significant and meaningful than the whole, which from an epistemological perspective helps us see that the whole is not just elusive but nonexistent and only ever understood through gross oversimplification or generalization.
I use these large-scale examples because the images are compelling. The scope is too large to be of value in the type of close level qualitative inquiry advocated by Clarke in her discussions of situational analysis. At a smaller scale, the significance of this type of analysis is vital to building complexity into what might at first pass seem a fairly simple context.
Also, in all of these examples the generative power I discuss might be found using other methods, but visualizations serve at least two functions: First, the activity of producing multiple renderings of the context surrounding a phenomenon destabilizes both the context and the phenomenon, an essential step toward shifting to more complex accounts of contemporary culture. Second, multiple layers of visualizations can provide a systematic trace of one’s movement through various analytical categories and interpretations. Whether or not one uses visually-oriented methods for thinking, the process, when woven into the findings as well as the analysis, highlights rather than hides the multiplicity of directions possible, offering one’s outcomes as a deliberate choice among many for what constitutes the research object.
 In fact, although some information is offered to explain the meaning of the size of node, thickness of lines, or placement of information, none of these visualizations describe in any detail the methods used to collect, cull, and analyze the data, or the decision process behind the choice and arrangement of particular elements to the exclusion of others. There is much room for critique, but I do not address it in this essay.
Clarke, A. (2003). Situational analysis: Grounded theory mapping after the postmodern turn. Symbolic Interaction, 26(4), 553-576.
Clarke, A. (2005). Situational analysis: Grounded theory after the postmodern turn. Thousand Oaks, California: Sage.