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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.

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).

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