  Recent research has focused on the monitoring of global-scale online data for
improved detection of epidemics, mood patterns, movements in the stock market,
political revolutions, box-office revenues, consumer behaviour and many other
important phenomena. However, privacy considerations and the sheer scale of
data available online are quickly making global monitoring infeasible, and
existing methods do not take full advantage of local network structure to
identify key nodes for monitoring. Here, we develop a model of the contagious
spread of information in a global-scale, publicly-articulated social network
and show that a simple method can yield not just early detection, but advance
warning of contagious outbreaks. In this method, we randomly choose a small
fraction of nodes in the network and then we randomly choose a "friend" of each
node to include in a group for local monitoring. Using six months of data from
most of the full Twittersphere, we show that this friend group is more central
in the network and it helps us to detect viral outbreaks of the use of novel
hashtags about 7 days earlier than we could with an equal-sized randomly chosen
group. Moreover, the method actually works better than expected due to network
structure alone because highly central actors are both more active and exhibit
increased diversity in the information they transmit to others. These results
suggest that local monitoring is not just more efficient, it is more effective,
and it is possible that other contagious processes in global-scale networks may
be similarly monitored.
