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Recruiting from the Network: Discovering Twitter Users Who Can Help Combat Zika Epidemics

  • Paolo Missier
  • Callum McClean
  • Jonathan CarltonEmail author
  • Diego Cedrim
  • Leonardo Silva
  • Alessandro Garcia
  • Alexandre Plastino
  • Alexander Romanovsky
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10360)

Abstract

Tropical diseases like Chikungunya and Zika have come to prominence in recent years as the cause of serious health problems. We explore the hypothesis that monitoring and analysis of social media content streams may effectively complement institutional disease prevention efforts. Specifically, we aim to identify selected members of the public who are likely to be sensitive to virus combat initiatives. Focusing on Twitter and on the topic of Zika, our approach involves (i) training a classifier to select topic-relevant tweets from the Twitter feed, and (ii) discovering the top users who are actively posting relevant content about the topic. In this short paper we describe our analytical approach and prototype architecture, discuss the challenges of dealing with noisy and sparse signal, and present encouraging preliminary results.

Keywords

Relevant Content Topic Focus Social Graph Social Sensor Disease Prevention Program 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Paolo Missier
    • 1
  • Callum McClean
    • 1
  • Jonathan Carlton
    • 1
    Email author
  • Diego Cedrim
    • 2
  • Leonardo Silva
    • 2
  • Alessandro Garcia
    • 2
  • Alexandre Plastino
    • 3
  • Alexander Romanovsky
    • 1
  1. 1.School of Computing ScienceNewcastle UniversityNewcastle upon TyneUK
  2. 2.PUC-RioRio de JaneiroBrazil
  3. 3.Universidad Federal FluminenseNiteròiBrazil

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