Select your localized edition:

Close ×

More Ways to Connect

Discover one of our 28 local entrepreneurial communities »

Be the first to know as we launch in new countries and markets around the globe.

Interested in bringing MIT Technology Review to your local market?

MIT Technology ReviewMIT Technology Review - logo

 

Unsupported browser: Your browser does not meet modern web standards. See how it scores »

{ action.text }

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, 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.

Predicting how widely a news story, or any other piece of information, will travel could help websites position their content and advertising more effectively, Leskovec says. It could also help determine influence of a writer or blogger, by showing how his or her content is shared. Combined with other work, it could help provide a better picture of how information travels online generally.

The researchers analyzed 170 million news articles and blog posts over the course of a year, and 580 million Twitter posts over eight months. They measured the attention each piece of content received by tracing how many times it was mentioned in other blog posts, news stories, and tweets. They did this not by looking at links, but by tracking the appearance of distinctive phrases—such as “lipstick on a pig”— in blog posts and articles. They used this data to create a graph that revealed six distinct patterns. Some stories, for example, spiked rapidly and then fell away, making a sharp, pointed shape. Others had more staying power, rising and falling more gently.

“By looking at when particular types of media get involved, you can see different patterns arise,” Leskovec says. For example, if a blog breaks a story, the pattern tends to be different than when a story is broken by a traditional news media. The point at which blogs get involved in a story, Leskovec says, is a major factor in determining its longevity. For example, even if traditional media focus on a story for a brief time, blog discussion can keep it in the public eye longer.

The early response to a new piece of content allowed the researchers to predict, with 75 percent accuracy, the shape of that item’s popularity over a longer period.

1 comment. Share your thoughts »

Credit: Stanford University

Tagged: Web, Internet, Twitter, media, network analysis

Reprints and Permissions | Send feedback to the editor

From the Archives

Close

Introducing MIT Technology Review Insider.

Already a Magazine subscriber?

You're automatically an Insider. It's easy to activate or upgrade your account.

Activate Your Account

Become an Insider

It's the new way to subscribe. Get even more of the tech news, research, and discoveries you crave.

Sign Up

Learn More

Find out why MIT Technology Review Insider is for you and explore your options.

Show Me