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Inferring Social and Psychological Meaning in Social Media

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911NF-20-C-0058
Agency Tracking Number: A2-8295
Amount: $1,099,992.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: A12A-T009
Solicitation Number: 12.A
Timeline
Solicitation Year: 2012
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-09-01
Award End Date (Contract End Date): 2022-09-01
Small Business Information
1050 W NASA Blvd Suite 155
Melbourne, FL 32901-1111
United States
DUNS: 038379579
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Tod Hagan
 (321) 409-5252
 thagan@securboration.com
Business Contact
 Lynn Lehman
Phone: (919) 244-3946
Email: llehman@securboration.com
Research Institution
 Dartmouth College
 Eugene Santos
 
11 Rope Ferry Road
Hanover, NH 03755-3510
United States

 (603) 646-6490
 Nonprofit College or University
Abstract

Health surveillance is the first line in detecting an epidemic and understanding its causes and progression and preventing a pandemic. It generally consists of systematic collection and reporting of population-based data for the purposes of detecting, characterizing, and countering health threats. While this seems sound in theory, by the time patients present with symptoms, laboratory specimens are obtained, tested and confirmed for the organism; exposure to the general populace is most likely past the “tipping point”, potentially heading towards epidemic and pandemic levels. The population is at the mercy of the pathogen and its inherent (and mutating) characteristics. In the case of COVID-19 these characteristics are extremely troublesome, leading to the near exponential infection curves; the emphasis on “flattening the curve”; and the near draconian measures taken to counter the pandemic. Unfortunately, epidemiological surveillance, analysis, and forecasting has not kept pace in terms of leveraging information in the digital age. There is a potential wealth of information embedded within social networks that can be leveraged for better surveillance, earlier detection, and more accurate forecasting. In this effort, we propose to adapt and extend SURF’s classification and deep learning capability to analyze social networks to help combat COVID-19 and future disease spread.

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

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