May 12, 2013

Too Much Philosophy?

The program at Web Science 2013 was diverse. For example, here’s the roster for the pecha kucha session:

From technical solutions to impetuous twittering to methodological questions in using Amazon Mechanical Turk to the nature of online prayer, we’re covering a lot of ground.

In the end, we have no choice. There are plenty of people who study some facet of the Web. Web Science studies the Web as an entire phenomenon. It’s not just the plumbing and it’s not just the sociology and its not just philosophy. Web Science it the place where philosophy informs the plumbing.

This makes for nifty sessions — you’ve got to love the transition between papers 5, 6, and 7 — but it also creates real tensions. A paper on the nature of trust, for example, simply cannot be correct in the way that a paper on information retrieval can. Then again, lots of people will be able to follow at least part of a paper about trust, but if you’ve forgotten what an eigenvalue is or why the intentional fallacy is false, it’s not hard to get lost in a paper whose author considers the argument straightforward.

In one of my first talks, I got a Nobel laureate completely confused about the elements of my experiment. That was a useful lesson: everyone has a hard time with hard ideas. You’ve spent months or years alone with your problem in a dark room, but your audience hasn’t met it before. Take it easy; they won’t be bored with a few minutes review and they won’t think you’re dim.

One significant problem at Web Science right now is a failure of imagination: how do our small studies suggest great consequences? This is not to say that writers should claim too much or write incautiously. But consequences that might rock your own province can strike people from other fields as obscure, and can seem pedantic or worse to people who have work to do.

Web Science is still not very good at working with people who build Web sites and invent Web apps, the very people we ought to be serving and to whom we ought to be listening. For that, we need every eigenvalue, every statistic, and every construct in our toolbox.