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 language | English |
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Title of host publication | Web Engineering - 17th International Conference, ICWE 2017, Proceedings |
Editors | Jordi Cabot, Roberto De Virgilio, Riccardo Torlone |
Publisher | Springer Verlag |
Pages | 437-445 |
Number of pages | 9 |
ISBN (Print) | 9783319601304 |
DOIs | |
Publication status | Published - 2017 |
Event | 17th International Conference on Web Engineering, ICWE 2017 - Rome, Italy Duration: 5 Jun 2017 → 8 Jun 2017 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10360 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 17th International Conference on Web Engineering, ICWE 2017 |
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Country/Territory | Italy |
City | Rome |
Period | 5/06/17 → 8/06/17 |
Bibliographical note
Publisher Copyright:© Springer International Publishing AG 2017.
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science