The past few years have seen the rapid rise of all things “social” on the web from the growth of online social networks like Facebook, to real-time communication services like Twitter, to user-contributed content sites like Flickr and YouTube, to content aggregators like Digg. Beyond these popular Web 2.0 successes, the emer- gence of Social Information Systems is promising to fundamentally transform what information we encounter and digest, how businesses market and engage with their customers, how universities educate and train a new generation of researchers, how the government investigates terror networks, and even how political regimes interact with their citizenry. Users have moved from being passive consumers of information (via querying or browsing) to becoming active participants in the creation of data and knowledge artifacts, actively sorting, ranking, and annotating other users and artifacts. This fundamental shift to social systems places new demands on providing de- pendable capabilities for knowing whom to trust and what information to trust, given the open and unregulated nature of these systems. The emergence of large-scale user participation in Social Information Systems suggests the need for the development of user-centric approaches to information quality. As a step in this direction this research proposes an interaction-based approach for modeling the notion of user im- portance. The interaction-based model is centered around the uniquely social aspects of these systems, by treating who communicates with whom (an interaction) as a core building block in evaluating user importance. We first study the interaction characteristics of Twitter, one of the most buzzworthy recent Social Web successes, examining the usage statistics, growth patterns, and user interaction behavior of over 2 million participants on Twitter. We believe this is the first large-scale study of dynamic interactions on a real-world Social Information System. Based on the anal- ysis of the interaction structure of Twitter, the second contribution of this thesis research is an exploration of approaches for measuring user importance. As part of this exploration, we study several different approaches that build on the inherent interaction-based framework of Social Information Systems. We explore this model through an experimental study over an interaction graph consisting of 800,000 nodes and about 1.9 million interaction edges. The user importance modeling approaches that we present can be applied to any Social Information System in which interactions between users can be monitored.