Viral science – things that spread fast.

Nature skips past the blue-and-black dress to ask: Have you seen the one about viral scholarship?:

In a paper due to appear in Management Science, Sharad Goel and his collaborators propose a mathematical definition of virality that quantifies the extent to which a concept is spreading between friends as opposed to via popular news outlets. Nature asked Goel, a computational social scientist at Stanford University in California, how his work applies to #TheDress.

What does ‘viral’ mean?

When people say viral they can mean a lot of different things. It’s often a synonym for popular. People will say, “Look at this viral video”, when really it was something released by Taylor Swift or something like that.

Something that’s a little bit closer to what I think of as viral, is something that’s not being promoted by a celebrity and that you wouldn’t ordinarily think is going to become very popular. Closer still — and this is what we get at in the paper — is something that it is diffusing mostly person-to-person, rather than through broadcasts such as those from the New York Times or other mainstream popular news outlets. We call this person-to person process structural virality.

There are lots of other features that might go into a more general notion of virality — for example the speed, the unpredictability — that are not included in this definition. The definition that we made I think encapsulates a lot of the core intuition behind virality but it certainly doesn’t capture all of it.

How do you determine whether something has ‘structural virality’?

We don’t say that something is or is not viral, but rather we measure virality on a continuous scale that goes from broadcast on one end to fully person-to-person on the other. In technical terms, the instances in which people shared a particular piece of content — by tweeting the link to a picture, say — forms a tree that traces out the spread of the content. To measure the level of virality, we calculate the average distance between pairs of sharing events in the tree. At one extreme, the pure broadcast case, all events are just two hops away from each other [because they can all be traced back to the same broadcast], so the average is two; at the other extreme, the average distance grows larger with the size of the tree.

Empirically, it’s very hard to measure these things. …

Why study virality?

Despite our collective, cultural fascination with viral events, we know surprisingly little about what drives them, how often they occur or even what the term ‘viral’ actually means. We now have both the data and the computational tools to shed light on these fundamental questions, which I find exciting.