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The Emerging Science of Superspreaders (And How to Tell If You're One Of Them)

Posted on by Brandon Klein

 Nobody has figured out how to spot the most influential spreaders of information in a real-world network. Now that looks set to change with important implications, not least for the superspreaders themselves.


Who are the most influential spreaders of information on a network? That’s a question that marketers, bloggers, news services and even governments would like answered. Not least because the answer could provide ways to promote products quickly, to boost the popularity of political parties above their rivals and to seed the rapid spread of news and opinions.

So it’s not surprising that network theorists have spent some time thinking about how best to identify these people and to check how the information they receive might spread around a network. Indeed, they’ve found a number of measures that spot so-called superspreaders, people who spread information, ideas or even disease more efficiently than anybody else.

But there’s a problem. Social networks are so complex that network scientists have never been able to test their ideas in the real world—it has always been too difficult to reconstruct the exact structure of Twitter or Facebook networks, for example. Instead, they’ve created models that mimic real networks in certain ways and tested their ideas on these instead.

But there is growing evidence that information does not spread through real networks in the same way as it does through these idealised ones. People tend to pass on information only when they are interested in a topic and when they are active, factors that are hard to take into account in a purely topological model of a network.

So the question of how to find the superspreaders remains open. That looks set to change thanks to the work of Sen Pei at Beihang University in Beijing and a few pals who have performed the first study of superspreaders on real networks.

These guys have studied the way information flows around various networks ranging from the Livejournal blogging network to the network of scientific publishing at the American Physical Society’s, as well as on subsets of the Twitter and Facebook networks. And they’ve discovered the key indicator that identifies superspreaders in these networks.