Using friends as sensors to detect global-scale contagious outbreaks

Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks. PLoS ONE. 2014. 9(4): e92413. doi:10.1371/journal.pone.0092413Recent 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, publiclyarticulated 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.This work was supported by a grant from the National Institute for General Medical Sciences (P-41 GM103504-03) (JHF) and a grant from the Pioneer Portfolio of the Robert Wood Johnson Foundation (NAC and JHF). MC acknowledges support from NICTA and the Australian Government, as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program; the DARPA/Lockheed Martin Guard Dog Program; and the Army Research Office under Grant W911NF-11-1-0363. EM acknowledges funding from Ministerio de Educacion y Ciencia (Spain) through projects i-Math, FIS2006-01485 (MOSAICO), and FIS2010-22047-C05-04. MGH acknowledges support from the Spanish Government (TIN2010-17344) and the R&D program of the Community of Madrid (S2009/TIC-1650). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript