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Recruiting from the network: Discovering Twitter users who can help combat Zika epidemics

Lookup NU author(s): Dr Paolo Missier, Professor Alexander Romanovsky

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).


Abstract

© Springer International Publishing AG 2017. 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.


Publication metadata

Author(s): Missier P, McClean C, Carlton J, Cedrim D, Silva L, Garcia A, Plastino A, Romanovsky A

Editor(s): Cabot J; De Virgilio R; Torlone R

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 17th International Conference on Web Engineering (ICWE 2017)

Year of Conference: 2017

Pages: 437-445

Print publication date: 01/06/2017

Online publication date: 01/06/2017

Acceptance date: 02/04/2017

ISSN: 0302-9743

Publisher: Springer Verlag

URL: https://doi.org/10.1007/978-3-319-60131-1_30

DOI: 10.1007/978-3-319-60131-1_30

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783319601304


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