Dynamic Consensus Analysis of Social Media (DCASM) for Rapid Crisis and Disaster Response Information Discovery

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$149,998.00
Award Year:
2012
Program:
SBIR
Phase:
Phase I
Contract:
N00014-12-M-0261
Award Id:
n/a
Agency Tracking Number:
N121-092-0291
Solicitation Year:
2012
Solicitation Topic Code:
N121-092
Solicitation Number:
2012.1
Small Business Information
3527 Beverly Glen Ter, Sherman Oaks, CA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
124668711
Principal Investigator:
AmosFreedy
President
(818) 788-4830
afreedy@percsolutions.com
Business Contact:
GershonWeltman
Vice President
(818) 788-1025
gweltman@percsolutions.com
Research Institute:
Stub




Abstract
The abundance of social media in crisis situations offers tremendous opportunity for filling the information vacuum that typically exists in HA/DR operations. However, current systems for extracting information are limited by human-in-the-loop processing of media, and do not provide automated methods for separating signal from noise, extracting information, and trust validation in order to create actionable intelligence. Furthermore, many computational approaches to extracting information do not leverage insights into human interaction and communication that have been uncovered by social scientists. We propose to apply social science algorithms, originally developed for ethnographic study, to the problem of extracting actionable intelligence from social media in disaster situations. A shared experience of reality, especially a profound reality such as a crisis scenario, should be reflected in observed message patterns. The proposed system will discover the latent"real"experience creating these patterns by adapting a number of rigorously validated analytic techniques. This will not only provide fully automated methods for processing and filtering social media data, but will also clarify collective understandings of reported reality for evaluating the trustworthiness of that information, for identifying meaningful aberrations in these reports, for effectively categorizing this information, and reliable intelligence for use by crisis response units.

* information listed above is at the time of submission.

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