Seeing the Unseen Research Community
Research that seeks to understand how to bring out transparency in knowledge organization, management and visualization; explores implications of keeping information unseen or hidden; and understand government policies on information and their implications.
John Bertot, Jordan Boyd-Graber, Jean Dryden, Ken Fleischmann, Jen Golbeck, Derek Hansen, Paul Jaeger, Jimmy Lin, Scott Paquette, Yan Qu, Ping Wang, Dave Yates
Aim: To integrate human knowledge into usable, useful machine learning algorithms.
Using topic models to discover, automatically, the trends and themes of large corporations.
Automatically discovering which documents in a collection are persuasive.
Aim: To develop a computational understanding of users' interactions and relationships in social networks.
Developing models to compute trust in social networks to make recommended systems socially intelligent.
Analyzing user behavior in Twitter to build models of information propagation in networks.
Aim: To study what makes IT innovations popular and the impact of popular innovations.
PopIT project: Developing a computational approach to understanding the diffusion of IT innovations--sponsored by National Science Foundation.
STICK project: Developing Science and Technology Innovation Concept Knowledge-base--sponsored by National Science Foundation.