Before May 1st, not even the smartest of machine learning algorithms could have predicted Keith Urbahn’s online relevancy score, or his potential to spark an incredibly viral information flow. While politicos “in the know” certainly knew him or of him, his previous interactions and size and nature of his social graph did little to reflect his potential to generate thousands of people’s willingness to trust within a matter of minutes. While connections, authority, trust and persuasiveness play a key role in influencing others, they are only part of a complex set of dynamics that affect people’s perception of a person, a piece of information or a product. Timing, initiating a network effect at the right time, and frankly, a dash of pure luck matter equally.
As SocialFlow noted, Urbahn’s tweet heard round the world was from an unlikely source. Just 1,000 people followed Urbahn prior to May 1. He’d hardly rank high on most influencer scores. In fact others had tweeted the same thing prior to Urbahn’s OBL tweet at 10:24pm. The conclusion from the SocialFlow researchers: Urbahn had a certain amount of trust in his position and, despite a small network, was connected with social media powerhouses like The New York Times reporter Brian Stelter and others. In other words, influence and trust are highly dependent on context.
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