Hello,

We noticed you're browsing in private or incognito mode.

To continue reading this article, please exit incognito mode or log in.

Not an Insider? Subscribe now for unlimited access to online articles.

Graphiti

Information's Social Highways

A startup studies the paths taken by viral messages

The thicker a line, the more retweets that connection generated. The larger the node, the more retweets that user's participation generated.

On the night last spring when Osama bin Laden was killed, the chief of staff to a former U.S. secretary of defense broke the news to the world—more than an hour before President Barack Obama’s announcement. Keith Urbahn (aka ­
@keithurbahn) wrote to his 1,016 Twitter followers that he’d heard the news from a “reputable person.” Within a minute, 80 people had reposted the message. One of them was New York Times reporter Brian Stelter, whose retweet led to another large burst of responses. Urbahn’s tweet was on its way to going viral.

There is no recipe for virality, says Gilad Lotan, head of R&D for a startup called SocialFlow, which aims to help clients from the Economist to Pepsi more effectively capture attention on Twitter. But the deluges of data that viral tweets generate hold potentially valuable insights into how and why certain things spread beyond their author’s network of regular contacts. After the bin Laden event, Lotan took advantage of ­SocialFlow’s access to the Twitter “fire hose,” a real-time stream of every tweet, to analyze—and visualize—the responses to Urbahn’s post. The results are seen on this page.

This story is part of our January/February 2012 Issue
See the rest of the issue
Subscribe

Each colored circle, or node, represents a Twitter user who repeated the original message (or posted something similar) and mentioned the author’s Twitter handle. The color gradient conveys how long it took for any given message to join the conversation; for instance, bluer circles represent people who took up Urbahn’s message within minutes.

Where circles are connected by a line, Lotan is representing the likely pathways along which the message passed. He determined them by analyzing, among other things, when each message was published and the relationships between users (who follows whom).

The larger the node, the more retweets that user's participation generated.

Lotan made the graph on this page using the same methodology. It shows responses to a tweet, posted by engineering professor Deb Chachra (@debcha), that resonated especially widely during last summer’s riots in Britain. This message spread much more slowly than the bin Laden news, and it spread without the involvement of a widely followed journalist. These differences are reflected in the diffuse shape and smaller clusters of the graph.

Being heard isn’t always easy in an age when anyone can become a broadcaster. But analyzing and visualizing such data helps SocialFlow guide customers about how, when, and what they should tweet to have the best chance of disseminating their messages widely.

Note: The orientation of the nodes was determined by a force-directed algorithm, a tool for organizing network graphs to aid visual understanding.

Get stories like this before anyone else with First Look.

Subscribe today
Already a Premium subscriber? Log in.

Uh oh–you've read all of your free articles for this month.

Insider Premium
$179.95/yr US PRICE

More from Intelligent Machines

Artificial intelligence and robots are transforming how we work and live.

Want more award-winning journalism? Subscribe to Insider Plus.
  • Insider Plus {! insider.prices.plus !}*

    {! insider.display.menuOptionsLabel !}

    Everything included in Insider Basic, plus ad-free web experience, select discounts to partner offerings and MIT Technology Review events

    See details+

    What's Included

    Bimonthly magazine delivery and unlimited 24/7 access to MIT Technology Review’s website

    The Download: our daily newsletter of what's important in technology and innovation

    Access to the magazine PDF archive—thousands of articles going back to 1899 at your fingertips

    Special discounts to select partner offerings

    Discount to MIT Technology Review events

    Ad-free web experience

/
You've read all of your free articles this month. This is your last free article this month. You've read of free articles this month. or  for unlimited online access.