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Recent research has focused on the monitoring of global-scale online data forimproved detection of epidemics, mood patterns, movements in the stock market,political revolutions, box-office revenues, consumer behaviour and many otherimportant phenomena. However, privacy considerations and the sheer scale ofdata available online are quickly making global monitoring infeasible, andexisting methods do not take full advantage of local network structure toidentify key nodes for monitoring. Here, we develop a model of the contagiousspread of information in a global-scale, publicly-articulated social networkand show that a simple method can yield not just early detection, but advancewarning of contagious outbreaks. In this method, we randomly choose a smallfraction of nodes in the network and then we randomly choose a "friend" of eachnode to include in a group for local monitoring. Using six months of data frommost of the full Twittersphere, we show that this friend group is more centralin the network and it helps us to detect viral outbreaks of the use of novelhashtags about 7 days earlier than we could with an equal-sized randomly chosengroup. Moreover, the method actually works better than expected due to networkstructure alone because highly central actors are both more active and exhibitincreased diversity in the information they transmit to others. These resultssuggest that local monitoring is not just more efficient, it is more effective,and it is possible that other contagious processes in global-scale networks maybe similarly monitored.

Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks