New analysis could help predict how stories will be shared.
Technology Review, January 14, 2011, By Erica Naone
These visualizations show the connections between blogs and news sites.
When a piece of news breaks online, it's hard to predict how widely it will be discussed in blog posts or tweets and for how long.
Jure Leskovec, an assistant professor of computer science at Stanford University, is working to find a way to make it easier to predict which pieces of content will resonate for a long time. A lot of factors go into that equation, however-the content of the story itself, the popularity of the site where the story originally appeared, and the nature of the community of readers at which it's aimed.
Two new research papers,
Modeling Information Diffusion in Implicit Networks, ICDM-10,written by Leskovec and Stanford PhD candidate Jaewon Yang, reveal patterns in the way news stories are shared online, which offer a way to predict early on how a story's popularity will rise and fall.
Patterns of Temporal Variation in Online Media, WSDM-11