Kristina Lerman

Research Summary

Much of my current research focuses on the social Web, a label that includes social media and online social networking sites. These technologies have transformed the Web into an active medium, where people create, annotate, evaluate and distribute information. The social Web is an exciting, complex domain for developing new AI algorithms and applications that leverage people's interactions as a platform for computing. My group studies novel behaviors and structures that emerge when people collectively organize and use information.

Learning from Social Metadata

Many social Web sites allow users to create content and annotate it with descriptive metadata, such as tags, and to organize content within personal hierarchies. Structured social metadata offers invaluable evidence for learning how a community organizes knowledge. But such metadata also tends to be sparse, shallow, ambiguous, noisy and inconsistent. We are developing machine learning methods to aggregate social metadata to improve information discovery and learn common taxonomies (folksonomies.)

Social Dynamics and Networks

As people interact on the social Web, their activity affects the structure of the Web itself, with complex feedback between individual and collective decisions producing qualitatively new online behaviors. We are developing a mathematical framework to model collective behavior, and to use these models for predicting trends, such as news story popularity. We are also developing novel metrics for network analysis and studying how network structure affects information spread.

Semantic Modeling of Information Sources

My research as a member of ISI's Information integration group deals with automatically recognizing semantics of data types used by various information sources. We use machine learning methods to represent and learn the structure of data extracted from information sources, and apply the learned representations to recognize new instances of the same data.