“Not everything that counts can be counted, and not everything that can be counted counts” – sign in Einstein’s Princeton office
This quote is from one of my favorite survey reminder postcards of all time, along with an image from from the Emilio Segre visual archives. The postcard layout was an easy and pleasant decision made in association with a straightforward survey we have conducted for nearly a quarter century. …If only social media analysis could be so easy, pleasant or straightforward!
I am in the process of conducting an ethnography of DC taxi drivers. I was motivated to do this study because of the persistent disconnect between the experiences and reports of the taxi drivers and riders I hear from regularly and the snarky (I know this term does not seem technical, but it is absolutely data motivated!) riders who dominate participatory media sources online. My goal at this point of the project is to chase down the disconnect in media participation and see how it maps to policy deliberations and offline experiences. This week I decided to explore ways of quantifying the disconnect.
Inspired by this article in jedem (the eJournal of eDemocracy and Open Government), I decided to start my search using framework based in Social Network Analysis (SNA), in order to use elements of connectedness, authority and relevance as a base. Fortunately, SNA frameworks are widely available to analysts on a budget in the form of web search engines! I went through the first 22 search results for a particular area of interest to my study: the mandatory GPS policy. Of these 22 sites, only 11 had active web 2.0 components. Across all of these sites, there were just two comments from drivers. Three of the sites that didn’t have any comments from drivers did have one post each that sympathized with or defended DC taxi drivers. The remaining three sites had no responses from taxi drivers and no sympathetic responses in defense of the drivers. Barring a couple of comments that were difficult to divine, the rest of the comments were negative comments about DC taxi drivers or the DC taxi industry. This matched my expectations, and, predictably, didn’t match any of my interviews or offline investigations.
The question at this point is one of denominator.
The easiest denominator to use, and, in fact, the least complicated was the number of sites. Using this denominator, only one quarter of the sites had any representation from a DC taxi driver. This is significant, given that the discussions were about aspects of their livelihood, and the drivers will be the most closely affected by the regulatory changes. This is a good, solid statistic from which to investigate the influence of web 2.0 on local policy enactment. However, it doesn’t begin to show the lack of representation the way that a denominator such as number of posts, number of posters, or number of opinions would have. But each one of these alternative denominators has its own set of headaches. Does it matter if one poster expresses an opinion once and another expresses another, slightly different opinion more than once? If everyone agrees, what should the denominator be? What about responses that contain links that are now defunct or insider references that aren’t meaningful to me? Should I consider measures of social capital, endorsements, social connectedness, or the backgrounds of individual posters?
The simplest figure also doesn’t show one of the most striking aspects of this finding; the relative markedness of these posts. In the context of predominantly short, snarky and clever responses, one of the comments began with a formal “Dear DC city councilmembers and intelligent taxpayers,” and the other spread over three dense, winding posts in large paragraph form.
This brings up an important aspect of social media; that of social action. If every comment is a social action with social intentions, what are the intentions of the posters and how can these be identified? I don’t believe that the majority of posts left were intended as a voice in local politics, but the comments from the drivers clearly were. The majority of posts represent attempts to warrant social capital using humor, not attempts to have a voice in local politics. And they repeated phrases that are often repeated in web 2.0 discussions about the DC taxi situation, but rarely repeated elsewhere. This observation, of course, is pretty meaningless without being anchored to the data itself, both quantitatively and qualitatively. And it makes for some interesting ‘next steps’ in a project that is certainly not short of ‘next steps.’
The main point I want to make here is about the nature of variables in social media research. Compared to a survey, where you ask a question, determined in advance, and have a set of answers to work with in your analysis, you are free to choose your own variables for your analysis. Each choice brings with it a set of constraints and advantages, and some fit your data better than others. But the path to analysis can be a more difficult path to take, and more justification about the choices you make is important. To augment this, a quantitative analysis, which can sometimes have very arbitrary or less clear choices included in it, is best supplemented with a qualitative analysis that delves into the answers themselves and why they fit the coding structure you have imposed.
In all of this, I have quite a bit of work out ahead of me.