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Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks

Author

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  • Manuel Garcia-Herranz
  • Esteban Moro
  • Manuel Cebrian
  • Nicholas A Christakis
  • James H Fowler

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.

Suggested Citation

  • Manuel Garcia-Herranz & Esteban Moro & Manuel Cebrian & Nicholas A Christakis & James H Fowler, 2014. "Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-7, April.
  • Handle: RePEc:plo:pone00:0092413
    DOI: 10.1371/journal.pone.0092413
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    References listed on IDEAS

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    Cited by:

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    3. Sun, Jiachen & Feng, Ling & Du, Mingwei & Ma, Xiao & Fan, Zhengping & Gloor, Peter & Hu, Yanqing, 2021. "Ultra-efficient information detection on large-scale online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    4. Naghmeh Momeni & Michael Rabbat, 2016. "Qualities and Inequalities in Online Social Networks through the Lens of the Generalized Friendship Paradox," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
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    6. Resce, Giuliano & Maynard, Diana, 2018. "What matters most to people around the world? Retrieving Better Life Index priorities on Twitter," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 61-75.

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