Research based on analysis of 170M news articles and 580M Twitter posts
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, 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.
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