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[CCICADA-announce] DIMACS/CCICADA Interdisciplinary Seminar Series - Tuesday, October 14, 2014

Linda Casals lindac at
Mon Oct 13 14:46:47 EDT 2014


DIMACS/CCICADA Interdisciplinary Seminar Series Presents
Title: Utilizing Social Media to Optimize Disaster Response

Speaker: Christie Nelson, Rutgers University

Date: Tuesday, October 14, 2014 12:00 - 1:00pm***

Location: DIMACS Center, CoRE Bldg, Room 431, Rutgers University                 
             Busch Campus, Piscataway, NJ

***Note New Time: 12:00 - 1:00pm


>From 1980 to 2011, the total loss from weather-related catastrophes was
$1,060 billion in 2011 USD and 30,000 people lost their lives in North
America. The number of weather-related catastrophes is only increasing,
with an increase factor of nearly 5 in North America over the past 30
years. In real-time scenarios an accurate picture of the situation is
needed quickly. Often during large-scale disasters, cell towers become
overloaded, and the only way of communication is through text
messages. It becomes important to gather information from text messages
sent to emergency numbers in order to respond quickly and efficiently
with life-saving efforts. In addition, responders are unable to manually
handle the large volume of incoming texts. Real-time information from
streaming data is needed, and responders would benefit from text
classification of incoming messages. To add to this difficult problem,
these data sources tend to be microtext, which makes the problem of
modeling the data more challenging.

The goal of this research was to develop a methodology to summarize text
messages sent during an emergency for use by responders, including
analysis of locations to identify geospatially potentially new areas of
population in need of emergency assistance. The real-time disaster needs
were then input into a mixed integer programming resource allocation
model for distribution of resources for disaster aid. Prior research
included resource allocation and text modeling, but the combination of
the two was a novel application not only in this arena, but more broadly
across domains. The model found the emerging real-time needs by
geolocation. Two methods were evaluated for determining these emergency
needs: a supervised method modeled the data with a variation of Naive
Bayes, Higher-Order Naive Bayes (HONB), and an unsupervised approach
modeled the data with a variation of Latent Dirichlet Allocation,
Higher-Order Latent Dirichlet Allocation (HO-LDA). It was found that
HONB performed better on domain relevant data than Naive Bayes, and
HO-LDA performed better than LDA. Also, the use of Higher-Order Learning
in conjunction with clustering geolocations to determine emerging
population centers during an emergency centralized response, which
reduced the unmet humanitarian aid need.

DIMACS/CCICADA Interdisciplinary Series Full Calendar

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