Researchers at Rutgers are leading an effort funded by the Department of Homeland Security to research techniques for monitoring social networks news articles, Web blogs and other social media for indicators of potential terrorist activity.
The Rutgers Center for Discrete Mathematics and Theoretical Computer Science will lead the team made up of researchers from the University of Southern California, the University of Illinois at Urbana-Champaign and the University of Pittsburgh. The group includes researchers from AT&T Laboratories, Bell Labs’/Lucent Technologies, Princeton University, Rensselaer Polytechnic Institute and Texas Southern University. Rutgers will get $1 million per year for three years. The DHS will fund the entire team $10.2 million over three years.” (source)
With the funds, the team has established the Center for Dynamic Data Analysis (DyDAn), one of four recently-announced University Affiliate Centers of the Institute for Discrete Sciences, which is a joint project between the Department of Homeland Security (DHS) and several national laboratories, led by Lawrence Livermore National Laboratory. DyDAn will coordinate the other three new University Affiliate Centers located at the University of Illinois, the University of Pittsburgh, and the University of Southern California.
“DyDAn researchers will develop new techniques for drawing inferences from massive flows of data arriving continuously over time. Buried in such data are patterns and behaviors that are changing, often quite quickly. The DyDAn team will develop novel technologies to find these patterns and relationships in dynamic and sometimes massive datasets. The DyDAn research program spans topics in Information Management and Knowledge Discovery as well as foundational topics in Discrete Mathematics.” (source)
Among the initial projects are two involving social media. One will study the problem of analyzing large, dynamic multigraphs that arise from blogs. Another will develop algorithms for identifying hidden social structures in virtual communities with a goal of finding hidden groups, coalitions and leaders by non-semantic analysis of large communication networks.