Transforming Large Scale Social Media Networks into Data-Drive, Dynamic Sensing Systems for Modeling and Predicting Real World Threats
Abstract
The project is to transform large scale social media networks into dynamic, data-driven sensing systems that model and predict both i) national security threats and ii) threats to the national energy infrastructure. Towards achieving this research goal, the research will:i) Acquire, store, and synthesize disparate data types (i.e., textual, image, temporal andgeospatial) generated within large scale social media networks in order to gain a fundamental understanding of what combination of different data types optimally models real world threats.ii) Test the hypothesis that real-world threat models, generated using social media networkdata, predict real world threat events with accuracies greater than X, where X is defined as baseline standard for modeling threats. i) national security threats and ii) threats to the national energy infrastructure will be the two research domains explored to test the hypothesis.iii) Protect the social media network model against cyber-attacks aimed at altering/diminishingits structural integrity and predictive capabilities. I.e., investigate the vulnerability of social media network models to misinformation dissemination that can potentially compromise their veracity and value to decision makers.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 09, 2018
- Source ID
- FA95501810108
Entities
People
- Conrad S. Tucker
Organizations
- Air Force Office of Scientific Research
- Pennsylvania State University
- United States Air Force