Whilst there are considerable doubts over the reliability of guides on creating viral content, there are no shortage of attempts to predict both how content spreads, and thus the kind of content that will do so.  A new study published recently by Stanford academics has set out to explore the viral nature of photos.

The researchers canvas of choice for this particular study was Facebook, and they wanted to test whether there were any predictable patterns behind the spread of photos on the social network.

“It wasn’t clear whether information cascades could be predicted because they happen so rarely,” says Jure Leskovec, assistant professor of computer science at Stanford University.

Data from the research revealed that just 5% of photos posted to Facebook get even a single share, with just 1 in 4,000 garnering more than 500 shares, which whilst still a decent number is not what one might describe as especially viral.

“It is very hard to quantify what going viral means,” says Leskovec. “Anyone would say ‘Gangnam Style’ went viral, but that’s a singular event,” he says, referring to the YouTube video that has been viewed almost 2 billion times.

From analysing some 150,000 Facebook photos that had received over 5 shares, the team believe they could predict whether photos would go viral with around 80% accuracy.

Their study suggests that the process works in stages.  At the early phase, there is perhaps a 50/50 chance that the number of shares for a photo will double.  The biggest factor, they believe, in the viral nature of photos is the speed with which photos were shared.

“Slow, persistent cascades don’t really double in size,” Leskovec says.

Despite this simple characteristic of virality in action, the team could come up with no discernable trait for a photo that made it go viral.

“Even if you have the best cat picture ever, it could work for your network, but not for my boring academic friends,” Leskovec says. “You have to understand your network.”