Detecting health events on the social web to enable epidemic intelligence

verfasst von

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.

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

Deutsche Akademie der Technikwissenschaften (acatech)
Università della Calabria

Typ

Aufsatz in Konferenzband

Seiten

87-103

Anzahl der Seiten

17

Publikationsdatum

2011

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Theoretische Informatik, Allgemeine Computerwissenschaft

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

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