Details
| Original language | English |
|---|---|
| Title of host publication | WEBIST 2011 - Proceedings of the 7th International Conference on Web Information Systems and Technologies |
| Pages | 571-576 |
| Number of pages | 6 |
| Publication status | Published - 14 Sept 2011 |
| Event | 7th International Conference on Web Information Systems and Technologies, WEBIST 2011 - Noordwijkerhout, Netherlands Duration: 6 May 2011 → 9 May 2011 |
Publication series
| Name | WEBIST 2011 - Proceedings of the 7th International Conference on Web Information Systems and Technologies |
|---|
Abstract
Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. However, state-of-the-art systems for Epidemic Intelligence have not kept the pace with the growing need for more robust public health event detection. Existing systems are limited in that they rely on template-driven approaches to extract information about public health events from human language text. In this paper, we propose a new approach to support Epidemic Intelligence. We tackle the problem of detecting relevant information from unstructured text from a statistical pattern recognition viewpoint. In doing so, we also address the problems associated with the noisy and dynamic nature of blogs by exploiting the language in moderated sources, to train a classifier for detecting victim reporting sentences in blog social media. We refer to this as Cross-Classification. Our experiments show that without using manually labeled data, and with a simple set of features, we are able to achieve a precision as high as 88% and an accuracy of 77%, comparable with the state-of-the-art approaches for the same task.
Keywords
- Automatic labeling, Cross-classification, Medical intelligence gathering
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Information Systems
Sustainable Development Goals
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WEBIST 2011 - Proceedings of the 7th International Conference on Web Information Systems and Technologies. 2011. p. 571-576 (WEBIST 2011 - Proceedings of the 7th International Conference on Web Information Systems and Technologies).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Exploiting the language of moderated sources for cross-classification of user generated content
AU - Stewart, Avaré
AU - Nejdl, Wolfgang
PY - 2011/9/14
Y1 - 2011/9/14
N2 - Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. However, state-of-the-art systems for Epidemic Intelligence have not kept the pace with the growing need for more robust public health event detection. Existing systems are limited in that they rely on template-driven approaches to extract information about public health events from human language text. In this paper, we propose a new approach to support Epidemic Intelligence. We tackle the problem of detecting relevant information from unstructured text from a statistical pattern recognition viewpoint. In doing so, we also address the problems associated with the noisy and dynamic nature of blogs by exploiting the language in moderated sources, to train a classifier for detecting victim reporting sentences in blog social media. We refer to this as Cross-Classification. Our experiments show that without using manually labeled data, and with a simple set of features, we are able to achieve a precision as high as 88% and an accuracy of 77%, comparable with the state-of-the-art approaches for the same task.
AB - Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. However, state-of-the-art systems for Epidemic Intelligence have not kept the pace with the growing need for more robust public health event detection. Existing systems are limited in that they rely on template-driven approaches to extract information about public health events from human language text. In this paper, we propose a new approach to support Epidemic Intelligence. We tackle the problem of detecting relevant information from unstructured text from a statistical pattern recognition viewpoint. In doing so, we also address the problems associated with the noisy and dynamic nature of blogs by exploiting the language in moderated sources, to train a classifier for detecting victim reporting sentences in blog social media. We refer to this as Cross-Classification. Our experiments show that without using manually labeled data, and with a simple set of features, we are able to achieve a precision as high as 88% and an accuracy of 77%, comparable with the state-of-the-art approaches for the same task.
KW - Automatic labeling
KW - Cross-classification
KW - Medical intelligence gathering
UR - http://www.scopus.com/inward/record.url?scp=80052590977&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:80052590977
SN - 9789898425515
T3 - WEBIST 2011 - Proceedings of the 7th International Conference on Web Information Systems and Technologies
SP - 571
EP - 576
BT - WEBIST 2011 - Proceedings of the 7th International Conference on Web Information Systems and Technologies
T2 - 7th International Conference on Web Information Systems and Technologies, WEBIST 2011
Y2 - 6 May 2011 through 9 May 2011
ER -