Detecting health events on the social web to enable epidemic intelligence

authored by

Marco Fisichella, Avaré Stewart, Alfredo Cuzzocrea, Kerstin Denecke

Abstract

Content analysis and clustering of natural language documents becomes crucial in various domains, even in public health. 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. Information should be gathered from a broader range of sources, including the Web which in turn requires more robust processing capabilities. To address this limitation, in this paper, we propose a new approach to detect public health events in an unsupervised manner. We address the problems associated with adapting an unsupervised learner to the medical domain and in doing so, propose an approach which combines aspects from different feature-based event detection methods. We evaluate our approach with a real world dataset with respect to the quality of article clusters. Our results show that we are able to achieve a precision of 62% and a recall of 75% evaluated using manually annotated, real-world data.

Organisation(s)

L3S Research Centre

External Organisation(s)

National Academy of Science and Engineering (acatech)
Università della Calabria

Type

Conference contribution

Pages

87-103

No. of pages

17

Publication date

2011

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Theoretical Computer Science, General Computer Science

Sustainable Development Goals

SDG 3 - Good Health and Well-being

Electronic version(s)

https://doi.org/10.1007/978-3-642-24583-1_10 (Access: Unknown)