Posted via web from alexis lloyd

Posted via web from alexis lloyd

Posted via web from alexis lloyd

Posted via web from alexis lloyd

Clay Shirky gave a very insightful talk on the future of the business model for news and journalism at Harvard this past weekend. He particularly talks about how the traditional model for newspapers was basically a fluke: a convergence of circumstances in the 20th century, rather than some kind of inherent truth about how news must operate. He argues that instead of creating one homogenous model to replace the old one, that it will and should be replaced by a multitude of smaller, overlapping solutions that together allow for accountability journalism to continue to exist in the ways that we need it to, as a society. In the meantime, though, he thinks things will fall apart much more before they come together in any coherent way, and we will see an increase in political corruption as a result. He particularly points to state and regional politics as an area that will be worst hit by this kind of corruption, because the new media landscape operates well at hyperlocal levels and at global levels, but not in that middle ground of state/region. The full text transcript is on the Nieman Lab website, but here are some excerpts that I found particularly interesting:

On content bundling in newspapers:

the coherence of newspapers is not intellectual, it’s industrial. Which is to say, if you’re running a website and somebody’s on your website and they just done a crossword puzzle and they seem to really like it, what’s the next thing you’re gonna show them? Is it news from Tegucigalpa? No. It’s another crossword puzzle. The idea that someone who is doing a crossword puzzle may also want news about the coup in Honduras or how the Lakers are doing — it doesn’t make any sense. It’s never made any sense, in terms of what the user wants. It’s what — it’s what print is capable of as a bundle. What goes into a print newspaper is the content that, on the margins, produces commercial interest in the least interested user. So, in the language of my tribe, the aggregation of news sources has gone from being a server-side to a client-side operation — which is to say, the decision about what to bring together into a bundle is made by the consumer and not at the level — and not by the producer.

On commercial, public (non-profit), and social models:

What the Internet does is it makes all commercial models of journalism harder to sustain — not impossible, but harder. And it makes public models easier to sustain — partly because of the lowered cost, partly because of the [inaudible]. And it makes social models much, much easier. So we’re seeing, I believe, a rebalancing of the landscape in terms of the logic of the creation of public goods away from a market dominated by commercial interest into a market where all three of these modes of production are going to be operating side by side in different ways.

On the gap between the death of old models and the birth of new ones:

I think a bad thing is going to happen, right? And it’s amazing to me how much, in a conversation conducted by adults, the possibility that maybe things are just going to get a lot worse for a while does not seem to be something people are taking seriously. But I think this falling into relative corruption of moderate-sized cities and towns — I think that’s baked into the current environment. I don’t think there’s any way we can get out of that kind of thing. So I think we are headed into a long trough of decline in accountability journalism, because the old models are breaking faster than the new models can be put into place.

There are a lot more interesting thoughts in there, but I’ll let you read them for yourself.

Because I was just assaulted with a page that looked like this. And oh yeah, that Wal-Mart ad? It’s a video, with no stop/pause button. And those two little ads? They move around randomly on the screen so you can’t close them.

Researchers at Cornell University have recently completed a huge study of 1.6 million news sites in which they tracked news stories in the three months leading up to the 2008 U.S. presidential election. They tracked stories on mainstream media sites as well as blogs, and saw some very interesting results. You can read about the study in detail at the Cornell Chronicle, but here are a few highlights:

• “They found a consistent rhythm as stories rose into prominence and then fell off over just a few days, with a “heartbeat” pattern of handoffs between blogs and mainstream media.”
• “In mainstream media, they found, a story rises to prominence slowly then dies quickly; in the blogosphere, stories rise in popularity very quickly but then stay around longer, as discussion goes back and forth.”
• “When a story first appears, there is a small rise in activity in both spheres; as mainstream activity increases, the proportion blogs contribute becomes small; but soon the blog activity shoots up, peaking an average of 2.5 hours after the mainstream peak.”

And the most interesting nugget, hidden towards the end of this article:

“Almost all stories started in the mainstream. Only 3.5 percent of the stories tracked appeared first dominantly in the blogosphere and then moved to the mainstream.”

This confirms something I have strongly suspected for a while. There’s a lot of talk about how mainstream media and institutionalized journalism have become unnecessary because everything can be done by bloggers, everything can be crowdsourced, etc., etc. But how to reconcile that with the fact that bloggers’ reporting is all based on stories from the mainstream media and institutionalized journalists? There is a clear dependence here that is being ignored. I wonder what would a day or a week without The New York Times look like in the blogosphere?

(cross posted at is it luck? and alekseistevens.com?)

My wife does a lot of work with data visualization, in which she extracts patterns and stories from large data sets and represents them visually through graphic design or animation.  It’s a really interesting and elucidating way of making sense out of huge amounts of information; in a sense, figuring out what stories the data tell on their own, rather than using data to support a preconceived idea.  Data visualizations can be pragmatic or artistic, and are often both.

I was thinking recently, though, about all the patterns that must exist in data that we don’t even know to look for, and what kinds of interesting stories they might tell us.  I am not a computer programmer, but it seems it must be possible to develop some kind of algorithmic pattern-seeker.  The human mind is constantly on the lookout for patterns, but has a pretty low fidelity, which is why we see Jesus in tree stumps and burnt toast and water stains, and why we think more weird stuff happens during a full moon than at other times, and all kinds of other very human logical fallacies. A machine, on the other hand, would not be susceptible to confirmation bias and other such pitfalls.

I wonder whether the next big development in data visualization is going to come when we can just feed enormous amounts of data into a system that will make its own sense out of it rather than requiring some kind of human intervention telling it what to look for.  This would be a boon both to artists and to scientists - to everyone concerned with parsing data and finding the larger truths they represent.  (If you know of a project like this that already exists or is in the works, please let me know in the comments!)

Data auralization doesn’t quite have the same ring to it (no pun intended), but it is something people do.  Sound artist and kinetic sculptor Trimpin, for example, created an installation in which sounds and musical robots are controlled by a live incoming stream of seismic data.  Another sound artist, Andrea Polli, created a piece that maps climate data to different sonic parameters in an algorithmic composition.  I myself have done interactive performance works where aspects of a player’s improvisation (eg pitch, loudness, tempo, number of attacks, etc) control an algorithmically generated electronic counterpoint.

But in all these cases, the computer is programmed to look at the incoming data stream (or the input data set) in a very particular way.  What might we end up hearing or seeing if the computer is allowed to look using its own logic?  What might we learn that we never would have zeroed in on left to our own devices?