[CSEE Talk] Fwd: CSEE Colloquium - Friday, April 19, 2013 at 1:00 pm - Oleg Aulov - Human Sensor Networks: Information Retrieval of Sensor Data from Heterogeneous Social Media Sources

[Yaacov Yesha]

CSEE Colloquium
Friday, April 19, 2013 at 1:00 pm
ITE 227, UMBC
Human Sensor Networks: Information Retrieval of Sensor Data from
Heterogeneous Social Media Sources

Oleg Aulov
Ph.D. student, UMBC

This talk will discuss the importance of different roles that social media
can play in management, monitoring, modeling and mitigation of natural and
human-caused disasters. A novel approach is presented that has high
potential for computational science to drastically improve disaster
management. In the proposed approach, social media sources are viewed as a
Human Sensor Network, and Social Media users are viewed as "human sensors"
that are "deployed" in the field, and their posts are considered to be
"sensor observations". These data can serve as a low-cost augmentation to
an observing system, which can be incorporated into geophysical models
together with other scientific data, such as satellite observations and
sensor measurements.
 Several use case scenarios are presented. In the case of the Deepwater
Horizon oil spill disaster that devastated the Gulf of Mexico, we gathered
the social media data that mentioned sightings of oil from Flickr,
geolocated them, and used them as boundary forcings in the General NOAA Oil
Modeling Environment (GNOME) software for oil spill plume movement
predictions. We showed how social media data can be incorporated into the
GNOME model to obtain improved estimates of the model parameters such as
rates of oil spill, couplings between surface winds and ocean currents,
diffusion coefficient, and other model parameters.
 In the case of the Tohoku Earthquake and Tsunami of 2011, the GNOME model
is modified to be used for tracking debris in the ocean instead of oil. The
social media data is to be assimilated into this model as initial
conditions from which the model is forecasting/hind-casting the movement.
Social media aspects of handheld devices such as Geiger counters that can
potentially detect radioactive debris will be discussed as well.
 In the case of Hurricane Sandy of 2012 that devastated the East Coast, we
have collected 8 million tweets and 370,000 Instagram images that mention
the hurricane. We will analyze and incorporate this data into the NOAA
storm surge model called SLOSH.
 Social media mining and citizen science projects performed by groups
outside of UMBC on air quality, earthquakes and the Fukushima disaster will
also be summarized as related work.

Oleg Aulov received a B.S. degree in mathematics from the University of
Central Missouri, Warrensburg, MO, in 2004 and an M.S. degree in Computer
Science with a concentration in Computer Security and Information Assurance
from George Washington University, Washington, DC, in 2006. He is currently
working toward a Ph.D. degree in the Department of Computer Science and
Electrical Engineering at University of Maryland, Baltimore County,
Baltimore, MD. His topics of interest include social media mining, citizen
science, machine learning, trust establishment and management, information
assurance, and social engineering.
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