Using friends as sensors to detect global-scale contagious outbreaks

Publication Date: 
April 2014
DOI: 10.1371/journal.pone.0092413
Manuel Garcia-Herranz
Esteban Moro Egido
Manuel Cebrian
James H. Fowler

Publisher Link >>


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-oce 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, but also more effective, and it may be applied to monitor contagious

processes in global-scale networks.