UCLA’s Networks and Network Analysis for the Humanities this past weekend did not fail to impress. Tim Tangherlini and his mathemagical imps returned in true form, organizing a really impressively realized (and predictably jam-packed) conference that left the participants excited, exhausted, enlightened, and unanimously shouting for more next year (and the year after, and the year after that, and the year after that…) I cannot thank the ODH enough for facilitating this and similar events.
Some particular highlights included Graham Sack’s exceptionally robust comparative analysis of a few hundred early English novels (watch out for him, he’s going to be a Heavy Hitter), Sarah Horowitz‘s really convincing use of epistolary network analysis to weave the importance of women (specifically salonières) in holding together the fabric of French high society, Rob Nelson’s further work on the always impressive Mining the Dispatch, Peter Leonard‘s thoughtful and important discussion on combining text and network analysis (hint: visuals are the way to go), Jon Kleinberg‘s super fantastic wonderful keynote lecture, Glen Worthey‘s inspiring talk about not needing All Of It, Russell Horton’s rhymes, Song Chen‘s rigorous analysis of early Asian family ties, and, well, everyone else’s everything else.
Especially interesting were the discussions, raised most particularly by Kleinberg and Hoyt Long, about what particularly we were looking at when we constructed these networks. The union of so many subjective experiences surely is not the objective truth, but neither is it a proxy of objective truth – what, then, is it? I’m inclined to say that this Big Data aggregated from individual experiences provides us a baseline subjective reality that provides us local basins of attraction; that is, trends we see are measures of how likely a certain person will experience the world in a certain way when situated in whatever part of the network/world they reside. More thought and research must go into what the global and local meaning of this Big Data, and will definitely reveal very interesting results.
My talk on bias also seemed to stir some discussion. I gave up counting how many participants looked at me during their presentations and said “and of course the data is biased, but this is preliminary, and this is what I came up with and what justifies that conclusion.” And of course the issues I raised were not new; further, everybody in attendance was already aware of them. What I hoped my presentation to inspire, and it seems to have been successful, was the open discussion of data biases and constraints it puts on conclusions within the context of the presentation of those conclusions.
Some of us were joking that the issues of bias means “you don’t know, you can’t ever know what you don’t know, and you should just give up now.” This is exactly opposite to the point. As long as we’re open an honest about what we do not or cannot know, we can make claims around those gaps, inferring and guessing where we need to, and let the reader decide whether our careful analysis and historical inferences are sufficient to support the conclusions we draw. Honesty is more important than completeness or unshakable proof; indeed, neither of those are yet possible in most of what we study.
There was some twittertalk surrounding my presentation, so here’s my draft/notes for anyone interested (click ‘continue reading’ to view):