Immediately, some behaviors stand out, explains Rodenbeck. Consider the broken horizontal white lines. These illustrate a story that hit the main page and is acquiring a series of diggs from various readers. However, the broken vertical white lines might represent suspicious digging behavior: they show an individual user digging a large number of stories–both newly submitted and older ones–in rapid succession. It’s improbable that one person produced so many diggs for such a large number of stories, Rodenbeck reasons. It’s far more likely that those diggs were automatically generated by bots, in an effort to artificially promote certain stories, he says.
“It gives us a pretty good picture of what’s going on,” says Rodenbeck, “but it’s only ever a partial picture.” There are many more parameters to map, he says. By mapping the same data using different metrics, such as a particular user’s recent activity or the number of contacts, or “friends,” on Digg that he or she has established, different types of patterns emerge. “We can not only get a more robust understanding of what’s currently happening in the Digg ecosystem, but get a better sense of what kinds of questions to ask moving forward,” Rodenbeck says.
So far the combination of citizen policing and data visualization has worked well to keep gaming on Digg relatively minimal. Although Digg doesn’t keep statistics on the number of gaming attempts since the site went live in late 2004, Rose says that “no organization has been able to successfully game Digg to our knowledge.”
Those users who are suspected of using their account(s) to try to game Digg are sent a warning e-mail. The user is banned after a second violation.
Rodenbeck thinks that cleverly graphing Digg’s social data helps in the fight against cheaters. “Visualization can’t solve the problem of gaming once and for all,” he says. “But it can definitely make the process of discovering patterns simpler, and we think there’s a lot of value in that.”