The mood workshop (microanalysis of online data) in Nijmegen last week was immensely helpful for me. In two short days, my research lost some branches and grew some deeper roots. Definitely worth 21+ hours of travel!
The retooling began early on the first day. My first, burning question for the group was about choosing representative data. The shocking first answer: why? To someone with a quantitative background, this question was mind blowing. The sky is up, the ground is down, and data should be representative. But representative of what?
Here we return to the nature of the data. What data are you looking at? What kind of motivated behavior does it represent? Essentially, I am looking at online conversation. We know that counting conversational topics is fruitless- that’s the first truth of conversation analysis. And we know that counting conversational participation is usually misguided. So what was I trying to represent?
My goal is to track a silence that happens across site types, largely independent of stimulus. No matter what kind of news article about taxis in Washington DC, no matter the source, the driver perspective is almost completely absent, and if it is represented the responses are noticeably different or marked. I had thought that if I could find a way to count this underrepresentation I could launch a systematic, grounded critique of the notion of participatory media and pose the question of which values were being maintained from the ground up. What is social capital in online news discourse, who speaks, and which speakers are ratified?
But this is not a question of representative sampling alone. Although sampling could offer a sense of context to the data, the meat and potatoes of the analysis are in fact fodder for conversation analysis. A more useful and interesting research question emerged: how are these online conversations constructed so as to make a pro taxi response dispreferred or marked? This question invokes pronoun usage, intertextuality, conversational reach, crowd based sanctioning, conversational structure and pair parts, register, and more. It provides grounding for a rich, layered analysis. Fertile soil from dry dirt. Thank you, Netherlands.