Recruiting from the network: Discovering twitter users who can help combat zika epidemics

Paolo Missier, Callum McClean, Jonathan Carlton*, Diego Cedrim, Leonardo Silva, Alessandro Garcia, Alexandre Plastino, Alexander Romanovsky

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationWeb Engineering - 17th International Conference, ICWE 2017, Proceedings
EditorsJordi Cabot, Roberto De Virgilio, Riccardo Torlone
PublisherSpringer Verlag
Pages437-445
Number of pages9
ISBN (Print)9783319601304
DOIs
Publication statusPublished - 2017
Event17th International Conference on Web Engineering, ICWE 2017 - Rome, Italy
Duration: 5 Jun 20178 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10360 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Web Engineering, ICWE 2017
Country/TerritoryItaly
CityRome
Period5/06/178/06/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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