Social software such as online forums, Wikis, and social networking sites, plays an important role in various fields, including science, politics, and education. Our goal is to analyze social activities within online communication and collaboration environments, and develop computational tools that support and promote effective interactions and participation.

In this presentation, we focus on online discussion modeling and intelligent tools for assisting discussion participants. We first analyze how messages and individual discussants contribute to Q&A discussions. We present a model for capturing information seeking or information providing roles of messages, such as question, answer or acknowledgement. We also identify user intent in the discussion as an information seeker or a provider. We show how the role information can be combined with linguistic and temporal features for developing a predictive model of discussant performance. We also demonstrate how such role information can be used for promoting interactions among seekers and potential peer mentors.

In the latter part of the presentation, we show how such analyses can be a powerful tool for social dialogue mediators and participants. In particular, we present a computational workflow framework that enables the efficient and robust integration of diverse datasets for the purpose of assisting discussion mediators, such as instructors who keep track of student online interactions.  Workflow results including unresolved issues or help seekers who need more assistance, are used to provide formative feedback to instructors to facilitate just in time mediator adaptation to discussants’  needs.