Startup Toolbox

Jay Parkhill September 18th, 2007

I wrote recently about different visualization techniques used by Digg, Lijit and Twitter. I wrote that I didn’t think the Twitter Blocks developer, Stamen Labs, got it quite right. They did a brilliant job with Digg’s visualizations so I’m sure they’ll work out Twitter as well.

The challenge in creating visual representations of text data, it seems to me, is to capture the essence of what the site does. Digg Stack beautifully captures both the flow of news across the Digg site and the voting element that (partially) distinguishes Digg from traditional news outlets.

Twitter is captivating for a couple of reasons. The “random discovery” element is fun- seeing what’s on people’s minds around the world. The more engaging element is following one’s friends.

Twitter Blocks goes after the latter, which is probably the harder nut to crack. Meanwhile, Twittervision hits the discovery nail right on the head. Watching the posts flow across the globe is mesmerizing.

A couple of requests, though- I’d like to see the tweets persist a little longer instead of fading out immediately when a new one comes up. I’d also like to see the history- it doesn’t seem to follow the Twitter timeline precisely and I can’t necessarily find interesting tweets easily.

If Stamen Labs can figure out how to combine Twittervision’s hypnotic visual timeline with the social relationship aspect that makes Twitter so engaging they will capture the full scope of the site perfectly. It’ll be fun to see.

• Web , social map , social media

Jay Parkhill September 3rd, 2007

Creating ways to visually represent the social map seems very much in vogue. It makes sense in a certain way; there is a lot of dispersed content on the web and good business to be had aggregating it. Visually presenting the relationships among pieces of content- and the users that put it there- can help people sort through it all.

Lijit and Twitter both just launched visualization tools that are interesting and have neat animation, but also point to how hard it is figuring out what kinds of data are can be visualized well.

Lijit’s visualizer shows linking relationships between a user and the rest of the Internet. Lijit’s focus is on bringing out content that might be hard to aggregate otherwise, so I can understand the value in trying to bring together inbound, outbound and mutual linking relationships on one page.

The resulting animation isn’t hugely meaningful, though. For example, this blog isn’t really linked from anywhere, so there is no benefit to the animation- it just shows what is in the blogroll on the page. The other blog I write, Startup Review for doesn’t link to anything else, so it only shows a handful of inbound links. And a very popular blog like Brad Feld’s has three different clusters of lollipops, but they’re still just lollipops. They don’t offer any information that you couldn’t get from a simple list and they are a bit cluttered to boot.

Twitter’s blocks are similar, though not quite as intuitive. There is a nice animation that creates a stair-step effect and I get that the center line is my recent timeline and the paths branching away are the timelines of other users in my timeline, but I’m not sure this is actually a better way of discovering other users. The bricks themselves don’t say anything until I zoom in on them, so they don’t save me time or present more/better data than linking through user pages directly. I.e. I can “explore” just as easily on the main pages.

For me Digg has set the gold standard here. Its swarm, stack, bigspy and arc all show what is happening on the site in a way that shows off Digg’s core competence- aggregating and ranking news- while letting users easily scan the news items flowing through the site without having to do anything.

Guy Kawasaki blogged an interesting article about data visualization methods. It’s an interesting read and some techniques definitely seem to do the job better than others, or maybe some data is just much harder to present visually.

My personal favorite is a time-sequence graph of Last.fm listening habits, If it was actually a dynamic graph it would nose out Digg for “best in class”. It isn’t though; it is a “snapshot” of a particular moment in time for the developer.

Still it is gorgeous and presents the information in a way that would take many more words to explain, and be far less interesting, and that is the point. The web is still mostly about words because words work pretty darn well. If the picture isn’t worth a heck of a lot of them, it doesn’t really add enough value.

• Web , social graph , social map