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First Principles Selection of Social Media Visualizations

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W31P4Q13C0198
Agency Tracking Number: D2-1214
Amount: $750,000.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: ST12A-004
Solicitation Number: 2012.A
Solicitation Year: 2012
Award Year: 2013
Award Start Date (Proposal Award Date): 2013-09-19
Award End Date (Contract End Date): 2015-12-17
Small Business Information
2361 Rosecrans Avenue, Suite 348
El Segundo, CA -
United States
DUNS: 053003017
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Pedro Szekely
 Project Leader / Research Asst Prof
 (310) 448-8641
Business Contact
 Steven Minton
Title: President
Phone: (310) 341-2446
Research Institution
 University of Southern California
 Pedro Szekely
4676 Admiralty Way Suite 1001
Marina del Rey, CA 90292-
United States

 (310) 448-8641
 Nonprofit College or University

As the importance of social networks skyrockets, so does the need to understand human behavior in these forums. While statistical analyses can help to some extent, many types of patterns are simply not visible by looking at raw numbers. However, many of these patterns do emerge in visualizations, and the innate human ability to see these patterns in graphical depictions represents a valuable opportunity for analysts. Yet the state of social network visualizations is in need of more creative approaches, beyond all-too-common node-link diagrams, which can often result in"hairball"visualizations that do not result in any useful insights. To address this need, we propose to build a fully integrated and deployable version of SocialViz, a first-principles powered visualization system we prototyped in Phase I of this STTR. SocialViz generates a large space of possible visualizations by combining various data properties in a multitude of ways, and then rely on cognitive first principles and human interest models to filter and sort through candidates for visualizations that are predicted to have high utility. SocialViz builds upon cognitive first principles, image analysis techniques, and recommender systems to deliver visualizations that are"interesting", both from a purely cognitive point of view, as well as from the view of that user. In building SocialViz, we will implement a cognitive measures model, a user interest model, and then combine these technologies in a stand-alone system that will be assessed by social network scientists as well as operational-minded subject matter experts. Based on their input, we will iteratively refine our system. Our overarching goal in this proposal is to engage human pattern detection ability to give us insight into social network behavior and to lead us to ask questions that we would have never asked when simply presented with statistics.

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

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