Browse by author
Lookup NU author(s): Professor Paolo MissierORCiD, Emeritus Professor Alexander RomanovskyORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
© 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.
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
Date deposited: 27/10/2017
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
