Supporting temporal analytics for health-related events in microblogs

authored by

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.

Organisation(s)

L3S Research Centre

External Organisation(s)

Monte S. Angelo University Federico II

Type

Conference contribution

Pages

2686-2688

No. of pages

3

Publication date

29.10.2012

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Software, Human-Computer Interaction, Computer Vision and Pattern Recognition, Computer Networks and Communications

Sustainable Development Goals

SDG 3 - Good Health and Well-being

Electronic version(s)

https://doi.org/10.1145/2396761.2398726 (Access: Unknown)