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Determining Causality between Online and Offline Behavior

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-23-P-0376
Agency Tracking Number: FX22D-OTCSO1-0288
Amount: $74,693.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: X22D-OTCSO1
Solicitation Number: X22.D
Timeline
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2022-11-03
Award End Date (Contract End Date): 2023-02-04
Small Business Information
1230 Avenue of the Americas
New York, NY 10020-1513
United States
DUNS: 079486132
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Vladimir Barash
 (301) 675-2534
 vlad.barash@graphika.com
Business Contact
 MARY GROENEMAN
Phone: (917) 704-6019
Email: elizabeth.walls@graphika.com
Research Institution
 Johns Hopkins University Applied Physics Laboratory
 Aurora Schmidt
 
11100 Johns Hopkins Road
Laurel, MD 20723-6099
United States

 (240) 228-5000
 Nonprofit College or University
Abstract

  Causal inference is a mathematical approach that enables the ability to understand how malign information operations online effectively seed and/or produce offline movements amongst a target population. This capability can tie explicit factors of an influence campaign to why a target population takes a certain side or more importantly, an offline action - e.g. starts a riot, ensures in person voting, etc. Understanding what drives populations behavior can enhance overall situational awareness of public social media data, enable more efficient decision-making, and foster  earlier (potential) intervention by the Air Force.  Graphika leverages the power of machine learning to create the world’s highest resolution maps of online landscapes. This platform discovers how communities form and maps how influence and information flow in real time within large-scale networks.

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

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