Early share signals predict how big a story will grow


Earlier this month, a 16-year-old lost his finger while dancing at a rave in a disused postal sorting office in London. He continued to dance, minus one finger, for another half hour to get value from the £10 he had paid to get in, before eventually being persuaded to go seek medical attention.

Later, he cheerfully Instagramed a picture of his pinkie-less hand from his hospital bed, which was soon picked up by the Independent in a story titled ‘Teen loses finger at Croydon rave, continues dancing ‘because the bass was hard‘.

Within an hour of the story being published, it had attracted over 1,600 interactions on Facebook.

Screenshot: NewsWhip Spike

Two hours later, the Facebook interaction count was up to almost 2,800, along with thousands of tweets. One week on, the story is the fourth most-shared piece in the UK, as well as the Independent’s biggest story by some distance, with over 60,000 Facebook engagements.

The Independent’s digital staff noticed the impact early on.

Social Media Editor Felicity Morse, who was responsible for posting the story to the Independent’s social media fans and followers, says speed was a crucial factor in the story’s success.

“It was obvious it was a great story right from the get go. You can’t really fool social media, it has to be a good story!” she told us.

“I like to think our story took off because our Facebook and Twitter followers are really engaged, they like sharing what we post and also because we sold the story on social in a way that reflected the way it was written and the way people were reading it. We were astonished by it and so was everybody else.”

Sharing in the first hour of the story – far higher than usual score for the Independent – had given some indication that this would be a big story. But could the scale of the early numbers actually predict how big the story would eventually become? Or perhaps the social velocity – the rate at which it spread in its first few minutes – held the key?


Accurately identifying viral stories before they actually go viral is no easy task.

But for newsrooms and other publishers increasingly focussed on maximising the spread of their content, it can be extremely useful.

Homepage and social media editors stand to attract huge numbers of new readers by understanding which of their stories is performing strongest, or spotting what is taking off for competitor sites.

But what’s the most reliable way of measuring viral potential?

Apart from Twitter quietly initiating a project to predict viral tweets, there has been little work by social networks themselves on predicting what stories that their users are sharing are about to go viral.

A June 18 Washington Post story on the news that the US Patent Office is to cancel the Washington Redskins’ trademark was tweeted over 3,200 times within 39 minutes of publication.

It was the biggest story of the week for the Post, attracting over 96,800 Facebook interactions and 8,000 tweets by June 25. So, could you have predicted that 39 minutes in?

The Speed of Human Sharing

At NewsWhip, we track and rank stories using Social Velocity, attributing scores to stories based on how fast they are spreading on Facebook, Twitter, and LinkedIn. Our dashboard (Spike) shows which stories are picking up shares fastest in hundreds of categories and sub-categories.

For some time, users of Spike – mainly journalists and editors – have told us that it has an uncanny ability to predict big stories, and find big stories early in the viral cycle. We wondered if we could quantify this effect.