Ethan Zuckerman wants you to eat your (news) vegetables — or at least have better information: I love this article, not only for the main idea (“dietary guidelines” for news), but for the sheer number of issues it raises about Information Architecture. It probably (and inadvertently) does the best job I’ve seen at showing how hard IA can be.
So, here’s the first-order problem – the problem we’re trying to solve through application of IA and analysis:
Ethan Zuckerman, the new director for the MIT Media Lab, wants to create some kind of system that shows you a summary of the topics of news you’re consuming. The idea is that you could see if you were deficient in any particular area – perhaps your consumption is tilted toward liberal sources, or perhaps you’ve been subconsciously avoiding news about the Arab Spring, etc.
This should be simple. Let’s get to it –
Sadly, the practical application of this theory gets complicated, very quickly. Yes, IA can get political:
First problem: As soon as you stick a label on something, it’s political.
“Anything tracked on a label is the subject of enormous lobbying,” he told me. “If you decide that you’re going to start tracking saturated versus unsaturated fat, there’s enormous controversy over that, and whether or not we should be subdividing out sodium and potassium….Everyone has vested interest.”
Once you get past that, there’s a problem in structuring information:
How do you design a hierarchy that is comprehensive but precise? Future-proof? At what point in the Tunisian revolution does the story become a category; and at what point does that category become a subcategory, of Arab Spring? Is a story about a medical study labeled Science, Health, Health & Science, or Medicine? Moreover, a hierarchy that works for one publication may not work for another. This is how even descriptive labeling gets political.
So, let’s just use some tagging and semantic analysis and let a computer figure it out! That’ll work, right? Well, no.
A story about the German economy might return the entities “Germany,” “economy,” “Angela Merkel,” and “Euro,” to name a few. A computer can easily pluck those entities out of text, but there are two problems: A human has to tell a computer what entities are important, one, and two, entities are not hierarchical. As far as a computer is concerned, “Angela Merkel” is not a subcategory of “German politicians” or “world leaders” — it’s just a flat piece of metadata.
Even if we get past that, where are we going to get all the data?
So where does he get all this data? When you’re talking about all of the news outlets in America, that’s a challenge. Zuckerman’s students were be able to build the New York Times contraption because they had access to publicly available, well-documented APIs (and were willing to use the Times’ existing taxonomy). Only a handful of major news organizations are so accessible.
And, back to the original problem, if we solve all these other problems, this one will work itself out, right? Well, no, probably not.
But does everyone want to see a digest of his own news nutrition, let alone look at someone else’s? I bet a lot of us are afraid of what it might say. Twenty-one years ago President Bush signed the Nutrition Labeling and Education Act. Many local laws now mandate posting calorie counts in chain restaurants. And America is still pretty fat.
I just loved this whole article. I love the problem he’s trying to solve, but I also loved the analysis of all the meta-problems that go into solving that other problem. So, to solve the first order problem, you have to solve about a dozen second-order IA problems.
Yes, it turns out that organizing information is hard. If anyone wants to know why IA projects can get so expensive, have them go read this article.
(Also, if this idea of balancing your news sources is interesting to you, read The Filter Bubble by Eli Pariser. It’s a great book that discusses the same thing in a much broader scale. Also, go read about homopily.)