Details
| Original language | English |
|---|---|
| Pages (from-to) | 61-71 |
| Number of pages | 11 |
| Journal | Global Journal of Flexible Systems Management |
| Volume | 18 |
| Issue number | 1 |
| Publication status | Published - 11 Jan 2017 |
Abstract
Social networks are becoming a valuable source of information for applications in many domains. In particular, many studies have highlighted the potential of social networks for early detection of epidemic outbreaks, due to their capability to transmit information faster than traditional channels, thus leading to quicker reactions of public health officials. Anyhow, the most of these studies have investigated only one or two diseases, and consequently to date there is no study in the literature trying to investigate if and how different kinds of outbreaks may lead to different temporal dynamics of the messages exchanged over social networks. Furthermore, in case of a wide variability, it is not clear if it would be possible to define a single generic solution able to detect multiple epidemic outbreaks, or if specifically tailored approaches should be implemented for each disease. To get an insight into these open points, we collected a massive dataset, containing more than one hundred million Twitter messages from different countries, looking for those relevant for an early outbreak detection of multiple disease. The collected results highlight that there is a significant variability in the temporal patterns of Twitter messages among different diseases. In this paper, we report on the main findings of this analysis, and we propose a set of steps to exploit social networks for early epidemic outbreaks, including a proper document model for the outbreaks, a Graphical User Interface for the public health officials, and the identification of suitable sources of information useful as ground truth for the assessment of outbreak detection algorithms.
Keywords
- Document models, Epidemic intelligence, Knowledge management, Methods for responsive and agile organizations, Social network analysis
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Business and International Management
- Business, Management and Accounting(all)
- Strategy and Management
Sustainable Development Goals
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In: Global Journal of Flexible Systems Management, Vol. 18, No. 1, 11.01.2017, p. 61-71.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Towards Exploiting Social Networks for Detecting Epidemic Outbreaks
AU - Di Martino, Sergio
AU - Romano, Sara
AU - Bertolotto, Michela
AU - Kanhabua, Nattiya
AU - Mazzeo, Antonino
AU - Nejdl, Wolfgang
N1 - Publisher Copyright: © 2017, Global Institute of Flexible Systems Management. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2017/1/11
Y1 - 2017/1/11
N2 - Social networks are becoming a valuable source of information for applications in many domains. In particular, many studies have highlighted the potential of social networks for early detection of epidemic outbreaks, due to their capability to transmit information faster than traditional channels, thus leading to quicker reactions of public health officials. Anyhow, the most of these studies have investigated only one or two diseases, and consequently to date there is no study in the literature trying to investigate if and how different kinds of outbreaks may lead to different temporal dynamics of the messages exchanged over social networks. Furthermore, in case of a wide variability, it is not clear if it would be possible to define a single generic solution able to detect multiple epidemic outbreaks, or if specifically tailored approaches should be implemented for each disease. To get an insight into these open points, we collected a massive dataset, containing more than one hundred million Twitter messages from different countries, looking for those relevant for an early outbreak detection of multiple disease. The collected results highlight that there is a significant variability in the temporal patterns of Twitter messages among different diseases. In this paper, we report on the main findings of this analysis, and we propose a set of steps to exploit social networks for early epidemic outbreaks, including a proper document model for the outbreaks, a Graphical User Interface for the public health officials, and the identification of suitable sources of information useful as ground truth for the assessment of outbreak detection algorithms.
AB - Social networks are becoming a valuable source of information for applications in many domains. In particular, many studies have highlighted the potential of social networks for early detection of epidemic outbreaks, due to their capability to transmit information faster than traditional channels, thus leading to quicker reactions of public health officials. Anyhow, the most of these studies have investigated only one or two diseases, and consequently to date there is no study in the literature trying to investigate if and how different kinds of outbreaks may lead to different temporal dynamics of the messages exchanged over social networks. Furthermore, in case of a wide variability, it is not clear if it would be possible to define a single generic solution able to detect multiple epidemic outbreaks, or if specifically tailored approaches should be implemented for each disease. To get an insight into these open points, we collected a massive dataset, containing more than one hundred million Twitter messages from different countries, looking for those relevant for an early outbreak detection of multiple disease. The collected results highlight that there is a significant variability in the temporal patterns of Twitter messages among different diseases. In this paper, we report on the main findings of this analysis, and we propose a set of steps to exploit social networks for early epidemic outbreaks, including a proper document model for the outbreaks, a Graphical User Interface for the public health officials, and the identification of suitable sources of information useful as ground truth for the assessment of outbreak detection algorithms.
KW - Document models
KW - Epidemic intelligence
KW - Knowledge management
KW - Methods for responsive and agile organizations
KW - Social network analysis
UR - http://www.scopus.com/inward/record.url?scp=85011596138&partnerID=8YFLogxK
U2 - 10.1007/s40171-016-0148-y
DO - 10.1007/s40171-016-0148-y
M3 - Article
AN - SCOPUS:85011596138
VL - 18
SP - 61
EP - 71
JO - Global Journal of Flexible Systems Management
JF - Global Journal of Flexible Systems Management
SN - 0972-2696
IS - 1
ER -