With the growth of online communities, the Web has evolved from networks of shared documents to networks of knowledge-sharing groups and individuals. A vast amount of heterogeneous yet interrelated information is being generated for which existing information analysis techniques are inadequate. Current tools often neglect the actual creators and consumers of information, and as a result, the findings are only useful to data analysts.
The user-centric Foreseer is the next generation of information analysis for online communities. It represents a new paradigm of study through the four "C's": content, context, crowd, and cloud. Information analysis of content is put into the context of the users' daily lives to benefit the communities (crowd) that generate information residing in the cloud. This project provides integrative and in situ analysis of information generated in online communities that is of the people, by the people, and for the people. Research of Foreseer consists of formal community models, efficient data analysis tools, advanced solutions of real applications, and novel information systems.
Making the results available to everyday Web users, not just data analysts, will result in improved dissemination of ideas, shared public opinions, and wise decision-making in online communities. Novel Web-based information systems will form prototypes that can be used in online social and health communities. The research will enhance the current information analysis and retrieval curricula and lead to a number of new classes in information science and health informatics. Research results will also be published on the project Web site (http://www-personal.umich.edu/~qmei/foreseer/).
