Identification

Author

Munro R, Gunasekara L, Nevins S, Polepeddi L, Rosen E

Title

Tracking epidemics with natural language processing and crowdsourcing

Year

2012

Publication type

Conference

Created

2012-05-01 16:09:14+00:00

Modified

2016-07-25 15:19:22.232317+00:00

Details

Organization

AAAI Spring Symposium Series

Access

Language

English

URL http://www.robertmunro.com/research/munro12epidemics.pdf
Accessed

2016-04-28

Extended information

Annote

The first indication of a new outbreak is often in unstructured
data (natural language) and reported openly
in traditional or social media as a new ‘flu-like’ or
‘malaria-like’ illness weeks or months before the new
pathogen is eventually isolated. We present a system for
tracking these early signals globally, using natural language
processing and crowdsourcing. By comparison,
search-log-based approaches, while innovative and inexpensive,
are often a trailing signal that follow open
reports in plain language. Concentrating on discovering
outbreak-related reports in big open data, we show how
crowdsourced workers can create near-real-time training
data for adaptive active-learning models, addressing
the lack of broad coverage training data for tracking epidemics.
This is well-suited to an outbreak information-
flow context, where sudden bursts of information about
new diseases/locations need to be manually processed
quickly at short notice.