digg labs, originally uploaded by densitydesign.

To understand what Digg labs is, is very important to understand what Digg is.
“Digg is a place for people to discover and share content from anywhere on the web. From the biggest online destinations to the most obscure blog, Digg surfaces the best stuff as voted on by our users. You won’t find editors at Digg — we’re here to provide a place where people can collectively determine the value of content and we’re changing the way people consume information online.”
Digg is all about sharing and discovery, there’s a conversation that happens around the content. We’re here to promote that conversation and provide tools for our community to discuss the topics that they’re passionate about. By looking at information through the lens of the collective community on Digg, you’ll always find something interesting and unique. We’re committed to giving every piece of content on the web an equal shot at being the next big thing.”

In four project it can be possible to see the quality of the complexity rappresentation about a big number of datas that are availables in real time. The dinamic infographic rappresentation is a good way to rappresent many layers of the information with an specific attention to the timeline principle which the dynamism is good able to emphasize.

Making sense of the activity on Digg is the mission behind Digg Labs. The Labs offer four different views of Digg data: Arc (shown at left), BigSpy, Stack, and Swarm. Like the Digg site itself, each visualization tracks similar information, including the newest stories that users “digg,” story popularity (number and frequency of “diggs”), and the names of “diggers” themselves. Best of all, the visualizations are in real-time, making the energy and behavior of the Digg community a palpable one. But while the tools give a new perspective on Digg activity, they fall short on helping users see any obvious patterns or draw specific conclusions. Some critics even consider them confusing. Despite the criticism, these data visualizations have provided direction on how to improve the Digg user experience, according to Digg creative director Daniel Burka:
“After seeing users congregate around stories and examining their relationships, we’ve tweaked our algorithms to take [content] diversity into account when determining how popular a story really is,” Burka says. This allows a wider range of subjects to show up on the home page, for example. “Many of the lessons we’ve learned in the Labs are also influencing future feature development and the general direction of the site.”