Supporting temporal analytics for health-related events in microblogs

verfasst von

Nattiya Kanhabua, Avaré Stewart, Wolfgang Nejdl, Sara Romano

Abstract

Microblogging services, such as Twitter, are gaining interests as a means of sharing information in social networks. Numerous works have shown the potential of using Twitter posts (or tweets) in order to infer the existence and magnitude of real-world events. In the medical domain, there has been a surge in detecting public health related tweets for early warning so that a rapid response from health authorities can take place. In this paper, we present a temporal analytics tool for supporting a comparative, temporal analysis of disease outbreaks between Twitter and official sources, such as, World Health Organization (WHO) and ProMED-mail. We automatically extract and aggregate outbreak events from official outbreak reports, producing time series data. Our tool can support a correlation analysis and an understanding of the temporal developments of outbreak mentions in Twitter, based on comparisons with official sources.

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

Università degli Studi di Napoli Federico II

Typ

Aufsatz in Konferenzband

Seiten

2686-2688

Anzahl der Seiten

3

Publikationsdatum

29.10.2012

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Software, Mensch-Maschine-Interaktion, Maschinelles Sehen und Mustererkennung, Computernetzwerke und -kommunikation

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1145/2396761.2398726 (Zugang: Unbekannt)