Nascent

Appidemiology

Could you use mathematical models from epidemiology to predict how an app is going to spread within the Facebook population?

People already talk about apps spreading virally. Admittedly my knowledge of epidemiology comes almost entirely from Wikipedia and an old episode of Numb3rs, but it seems like it’d all fit:

Your Facebook friends are your friends in real life (theoretically, anyway) so they tend to be clustered geographically – there’s the place you grew up, where you went to college, where your first job was… etc.

Apps spread primarily from friend to friend – either by exposure through a box on a user’s profile page (medium level exposure) or through invites (high exposure) and news stories (low exposure).

There are seasonal trends – well, daily trends. Mondays and Fridays are hot Facebook app time, when immune systems (common sense? Other things to do?) are low and infection is most likely to spread.

An example of an acute infection would be Zombies – you install it because all your friends have it, send out loads of invites and pretty soon afterwards you realize that it’s a bit rubbish and uninstall it (whereupon you’re cured and are no longer infectious). Chronic infections would be things like Grafitti and SuperPoke that get used all the time. Latent infections would be something like Flickster that only kicks in when you make an occasional update.

Seriously, at the University of Texas Lauren Ancel Meyers is already using social graphs to to model infectious disease:

“Each person within a community is represented as a point in the network,” Meyers explains. “The edges that connect a person to other people represent interactions that take place inside or outside of the home, including interactions that take place at school or work, while shopping or dining, while at a hospital, etc. The network thereby captures the diversity of human contacts that underlie the spread of disease.”

There are obvious problems with trying to capture all of the significant interactions inside real life communities. Not such problems for Facebook, where interactions are all stored in convenient news feed form.

Some people may come into contact with very few people, but others may have many strands connecting them to other people in the community through their work or social habits. If this person becomes sick, he or she has the potential to become what researchers call a “superspreader,” someone who spreads disease to a lot of people in the community. Identifying potential superspreaders is one step in curbing an outbreak (ed: towards knowing who to market to.)

Any epidemiologists or Facebook app developers interested in investigating further? AOP Publishing Awards here we come…

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