We all know that it’s frequently easier to understand data if it is presented in visual form. We draw diagrams, graph data, and make maps to show how information is related in various ways. A few day ago, I was commenting about the WordPress development timeline, and wanted to show how the updates are getting closer and closer together. The best way to show this was to use a line graph, and it made the point fairly well.
How does data visualization apply to a social networking site, though? Digg users can use the neat tools at Digg Labs to easily see what’s new and what’s popular on the site.
Let me back up for a minute. I don’t think I’ve ever blogged about Digg, so I should explain what it is. Digg is a site where people can submit links to news stories, web sites, or other online resources. Other users can then look at these sites and “digg” or “bury” them. Users can also comment on the links. As stories get more popular, they’re promoted. As they become less popular, they are demoted. Time also plays a role in the algorithm, so newer submissions are more likely to rise to the top of the list. The front page of Digg always contains the most popular items at any given time.
Essentially, Digg gives the community collective editorial control. Items that the community values end up on the front page, and those the community doesn’t value fall down the list.
Back to data visualization. BigSpy shows Digg items in real time. Every time someone diggs a story, it shows up on the list, and everything else scrolls down. The more diggs an item has, the larger the typeface. So the result is a scrolling window showing, in real time, what people are digging.
Swarm shows who is digging what. Each Digg item is represented by a small circle on the screen and the Digg users who are digging the items show up as yellow dots. Popular items get larger, and have more users attached to them. When you move the mouse over one of the items, it gives you a brief description of it, and shows how it’s related to the other items.
Stack takes a different approach. It still shows Digg activity in real time. But this one shows each digg as a dot falling Tetris-like down the screen. The most popular stories create the tallest stacks at the bottom. All of the diggs are represented by title below the graph as they happen. Color is used to represent the total number of diggs, while the height of the stack indicates the number of diggs occuring right now.
The newest of the Digg visualization tools is Arc. This tool represents Digg data as a segmented circle. The amount of space each item takes along the edge of the circle is determined by the item’s popularity. Again, each item is updated in real time, and historical data is shown as lines eminating from the circle.
Perhaps these aren’t the best examples of data visualization. While it’s interesting to see what the Digg users are finding interesting at any given moment, you can waste an enormous amount of time playing with Digg. But these tools do illustrate how developers are finding new and innovative ways to represent data in real time.
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