UMBC ebiquity
Presentation
Understanding RSM: Relief Social Media
September 15, 2009
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This presentation describes a new ONR-sponsored two-year research project
'Understanding RSM: Relief Social Media' that we are beginning in
conjunction with colleagues at the Lockheed Martin Advanced Technology
Laboratory. The RSM project is aimed at helping to detect and monitor
information about crises and associated relief efforts from online
sources including both social media and main stream media.
There has been a dramatic rise in the use of online social media
systems like Twitter, Facebook, and Flickr are increasingly being used
to describe and document events related to natural disasters,
political turmoil, terrorist attacks, pandemics and other crises as
they unfold.
Often these sources provide the only reliable stream of timely
information about what is actually happening on the ground. Mobile
phones are being used to capture and upload pictures, audio and videos
which are often annotated with metadata accurately capturing the time
and location as well as semantic tags. At the same time, citizen and
professional responders in relief scenarios are using the same
technologies to report data and coordinate their activities. In
effect, these systems enable the population to act as a human sensor
network.
The project has three tasks. The first is to build a system to harvest
empirical evidence about crises, resulting in a unique cross-system
data corpus, whose empirical characterization can be used to guide
further research and engineering. The second task will focus on
creating a computational model to codify our theoretical understanding
of the dynamics of these systems. A software simulator of the abstract
model will be used to generate virtual network instances to experiment
against. Finally, we will develop a analysis toolkit that will allow
us to support forecasting and planning of decision makers. In
particular, we can generate appropriate confidence levels and model
both information source influence and bias in content from open sources.
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