The popularity of YouTube videos follows a similar pattern, albeit on a longer timescale. By looking at the number of views a video gets on its first day, the researchers could determine the likelihood of reaching a certain level of popularity after a longer period. For instance, if only 15 people view the video during the first day, it’s unlikely to become a big hit. However, if more than 100 people see it on the first day, then there’s a high likelihood that it will be seen tens of thousands of times more.
In the case of both YouTube and Digg, Huberman notes that predictions become more accurate as data are considered over longer periods. For instance, within seven hours, it is possible to predict a story’s future popularity on Digg nearly perfectly. Likewise for videos posted to YouTube for 20 days.
Huberman says that other sites, such as online stores, would need their own ways of determining the popularity of their content because each site has unique characteristics. But advertisers, he says, armed with popularity predictions, could quickly determine which products might “go viral” and then tag special ads to those.
“I think popularity prediction is an interesting topic in general,” says Claudia Perlich, researcher at IBM’s Watson Research Center in Yorktown Heights, NY. “And I certainly see the value [of] predicting popularity for advertising.” However, she notes that it’s only one part of the advertising puzzle. Increasingly, she says, advertisers are interested in the type of people who are viewing the content, and they find it useful to know the path that a Web surfer has taken before arriving at a site, so that he or she can be better targeted with a specific ad.
Perlich also raises questions about the two systems under study. “I have the slight worry that the results are driven by underlying technology,” she says. It could be possible that the researchers are simply measuring the proprietary process in which stories become popular on Digg and some of the video-promotion features of YouTube. This is the problem, she says, with doing an experiment in which the systems are proprietary and it’s impossible to know exactly how they work.
Huberman is confident that his methodology can predict popularity independently of specific algorithms used by the sites. He is in the process of analyzing the popularity of people on Twitter, a microblogging service that lets people post short messages to one another and subscribe to updates. Better understanding of these social networks, he says, could lead to entirely new business models. “The only thing that’s of value today is people’s attention,” he says. “An immense amount of money is spent on trying to draw our attention to things.”