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Automating the Application of Deception Detection Heuristics to Unstructured Data

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-10-M-0304
Agency Tracking Number: N10A-029-0105
Amount: $69,961.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N10A-T029
Solicitation Number: 2010.A
Solicitation Year: 2010
Award Year: 2010
Award Start Date (Proposal Award Date): 2010-06-28
Award End Date (Contract End Date): 2011-04-30
Small Business Information
951 Mariner's Island Blvd., STE 360
San Mateo, CA 94404
United States
DUNS: 608176715
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Terrance Goan
 Principal Investigator
 (206) 545-1478
Business Contact
 Carolyn Maxwell
Title: Contracts Manager
Phone: (650) 931-2700
Research Institution
 The University of Washington
 Lynne Chronister
4333 Brooklyn Avenue NE Box 359472
Seattle, WA 98195
United States

 (206) 543-4043
 Nonprofit College or University

We propose to construct a deception detection system which will exploit scaffolding provided by a collection of largely domain-independent deception detection heuristics. These heuristics, integrated through a novel evidential reasoning system, will provide the proposed system, called Skeptic, with a significant advantage over purely inductive methods by allowing it to exploit the adversarial nature of the problem. Whereas previous systems have only provided coarse level judgments regarding the deceptive text communications, Skeptic will employ a mix of lightweight natural language processing and information extraction techniques to allow for the detection of misleading information present in otherwise truthful communications. Further, Skeptic will adapt over time, which means it can be deployed early, and mature as the understanding of the different operational contexts matures. In this work we will exploit our team’s substantial software assets and experience in the areas of text analysis and machine learning, as well as our very-specifically related experience developing a system for detecting a particular class of deception called “stock pumping.” Given this foundation we are able to propose an aggressive work plan that will result in a proof-of-concept demonstration against multiple existing datasets.

* Information listed above is at the time of submission. *

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