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PLoS One. 2014 Apr 9;9(4):e92413. doi: 10.1371/journal.pone.0092413. eCollection 2014.

Using friends as sensors to detect global-scale contagious outbreaks.

Author information

1
Department of Computer Science, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain.
2
Department of Mathematics & GISC, Universidad Carlos III de Madrid, Leganés, Spain; Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, Madrid, Spain.
3
Computer Science & Engineering Department, University of California San Diego, San Diego, California, United States of America; Media Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America; National Information and Communications Technology Australia, Melbourne, Victoria, Australia.
4
Department of Sociology, Yale University, New Haven, Connecticut, United States of America; Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, United States of America; Department of Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
5
Medical Genetics Division, School of Medicine, University of California San Diego, San Diego, California, United States of America; Political Science Department, University of California San Diego, San Diego, California, United States of America.

Abstract

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, but also more effective, and it may be applied to monitor contagious processes in global-scale networks.

PMID:
24718030
PMCID:
PMC3981694
DOI:
10.1371/journal.pone.0092413
[Indexed for MEDLINE]
Free PMC Article

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