Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE 30th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2016 |
| Editors | Antonio J. Jara, Makoto Takizawa, Yann Bocchi, Leonard Barolli, Tomoya Enokido |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 69-74 |
| Number of pages | 6 |
| ISBN (electronic) | 9781509018574 |
| Publication status | Published - May 2016 |
| Event | 30th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2016 - Crans-Montana, Switzerland Duration: 23 Mar 2016 → 25 Mar 2016 |
Abstract
Many studies have indicated the potential of using Social Networks for the early detection of public health events, such as epidemic outbreaks, so that a faster response can take place. Anyhow, the most of these studies are focused on one or two diseases, and consequently to date it is not clear if and how different outbreaks give rise to different temporal dynamics of the messages. Furthermore, it is not clear if it is possible to define a single generic Data Mining solution for the detection of epidemic outbreaks from this Big Data, or if specifically tailored approaches should be implemented for each disease. To get an insight on this issue, we collected a massive dataset of Twitter messages to extract relevant information regarding different outbreaks from different countries in 2011. The manual analysis we conducted allowed us to define some macro-classes of diseases. Results show that there is a considerable variability in the temporal dynamics of Twitter messages from different diseases, and that the identification of a suitable source of information, to define a ground truth suitable for the assessment of time series analysis algorithms, is a challenging task. Finally we also report on a special case we found, highlighting that a lot of research has still to be done in this field.
Keywords
- Big data, Social media mining
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Information Systems
- Decision Sciences(all)
- Information Systems and Management
- Mathematics(all)
- Modelling and Simulation
Sustainable Development Goals
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Proceedings - IEEE 30th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2016. ed. / Antonio J. Jara; Makoto Takizawa; Yann Bocchi; Leonard Barolli; Tomoya Enokido. Institute of Electrical and Electronics Engineers Inc., 2016. p. 69-74 7471175.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Challenges in Detecting Epidemic Outbreaks from Social Networks
AU - Romano, Sara
AU - Martino, Sergio Di
AU - Kanhabua, Nattiya
AU - Mazzeo, Antonino
AU - Nejdl, Wolfgang
PY - 2016/5
Y1 - 2016/5
N2 - Many studies have indicated the potential of using Social Networks for the early detection of public health events, such as epidemic outbreaks, so that a faster response can take place. Anyhow, the most of these studies are focused on one or two diseases, and consequently to date it is not clear if and how different outbreaks give rise to different temporal dynamics of the messages. Furthermore, it is not clear if it is possible to define a single generic Data Mining solution for the detection of epidemic outbreaks from this Big Data, or if specifically tailored approaches should be implemented for each disease. To get an insight on this issue, we collected a massive dataset of Twitter messages to extract relevant information regarding different outbreaks from different countries in 2011. The manual analysis we conducted allowed us to define some macro-classes of diseases. Results show that there is a considerable variability in the temporal dynamics of Twitter messages from different diseases, and that the identification of a suitable source of information, to define a ground truth suitable for the assessment of time series analysis algorithms, is a challenging task. Finally we also report on a special case we found, highlighting that a lot of research has still to be done in this field.
AB - Many studies have indicated the potential of using Social Networks for the early detection of public health events, such as epidemic outbreaks, so that a faster response can take place. Anyhow, the most of these studies are focused on one or two diseases, and consequently to date it is not clear if and how different outbreaks give rise to different temporal dynamics of the messages. Furthermore, it is not clear if it is possible to define a single generic Data Mining solution for the detection of epidemic outbreaks from this Big Data, or if specifically tailored approaches should be implemented for each disease. To get an insight on this issue, we collected a massive dataset of Twitter messages to extract relevant information regarding different outbreaks from different countries in 2011. The manual analysis we conducted allowed us to define some macro-classes of diseases. Results show that there is a considerable variability in the temporal dynamics of Twitter messages from different diseases, and that the identification of a suitable source of information, to define a ground truth suitable for the assessment of time series analysis algorithms, is a challenging task. Finally we also report on a special case we found, highlighting that a lot of research has still to be done in this field.
KW - Big data
KW - Social media mining
UR - http://www.scopus.com/inward/record.url?scp=84983546558&partnerID=8YFLogxK
U2 - 10.1109/waina.2016.111
DO - 10.1109/waina.2016.111
M3 - Conference contribution
AN - SCOPUS:84983546558
SP - 69
EP - 74
BT - Proceedings - IEEE 30th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2016
A2 - Jara, Antonio J.
A2 - Takizawa, Makoto
A2 - Bocchi, Yann
A2 - Barolli, Leonard
A2 - Enokido, Tomoya
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2016
Y2 - 23 March 2016 through 25 March 2016
ER -