Social Media Analysis and Reasoning Tool (SMART)

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$99,800.00
Award Year:
2013
Program:
STTR
Phase:
Phase I
Contract:
W911NF-13-C-0023
Agency Tracking Number:
A12A-009-0137
Solicitation Year:
2012
Solicitation Topic Code:
A12a-T009
Solicitation Number:
2012.A
Small Business Information
Charles River Analytics Inc.
MA, Cambridge, MA, 02138-4555
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
115243701
Principal Investigator:
Avi Pfeffer
Principal Scientist
(617) 491-3474
apfeffer@cra.com
Business Contact:
Mark Felix
Contracts Manager
(617) 491-3474
mfelix@cra.com
Research Institution:
Boston College
Susan Hoban
140 Commonwealth Ave.
Boston, MA, 02467-
(617) 552-3061
Nonprofit college or university
Abstract
The proliferation of social media technologies has provided a promising new source of information for intelligence analysts. Effective use of this data could provide insight into the evolution of social networks, identification of communication patterns that lead to events of interest, and analysis of relevant socio-cultural factors and trends. However, intelligence analysts frequently have difficulty making predictions or drawing substantive analytical conclusions from social media data since they lack a solid theoretical framework in which to formulate their hypotheses. Furthermore, the richness, immediacy, and diversity found in social media data add to the challenge analysts face. To assist analysts in deriving actionable information from social media data, tools are needed that are grounded on a sound theoretical framework. The tool must employ these theories effectively to transform raw data into intelligence, and must be linked to operational decision makers"information needs and support the analyst in communicating their insights with respect to those needs. Charles River Analytics, in partnership with Boston College, proposes a Social Media Analysis and Reasoning Tool (SMART). SMART uses innovative theories on social media and real-world networks to guide cutting-edge semi-supervised machine learning algorithms that derive useful inferences from social media in support of operational decision-making.

* information listed above is at the time of submission.

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