Predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially “viral” ideas or behaviors, are critically important for both national and global security. An enormous volume of security-relevant information is present on the Web, for instance in the content produced each day by millions of bloggers worldwide, but discovering and making sense of these data is very challenging. Detecting and characterizing emerging topics of discussion and consumer trends through analysis of Internet data is also of great interest to businesses. Non-equilibrium social science emphasizes dynamical phenomena, for instance the way political movements emerge or competing organizations interact.
This research and development involves web analysis, security informatics, network analysis, text analysis, supervised/unsupervised learning, cyber security, sentiment analysis, social media analysis, machine learning, sociological models, and social network modeling. The work has produced a new method for estimating sentiment and/or emotion expressed in social media which addresses the challenges associated with Web-based analysis
The image at right illustrates the process of modeling complex contagion on networks with community structure via stochastic hybrid dynamical systems (S-HDS). The cartoon at top left depicts a network with three communities. The cartoon at bottom illustrates contagion within a community k and between communities i and j. The schematic at top right shows the basic S-HDS feedback structure.