Engagement and Incentives in Online Community: Observational Data, Prediction Models, and Field Experiments.

This proposal aims to study user engagement pattern and how different incentive mechanisms influence user behavior in online communities. Work in this proposal investigates the diverse behavior patterns that different individuals follow in various online communities, and how incentive design can help increase user engagement. First, our work on MOOCs leads to the discovery of behavioral heterogeneity in students course selection as well as their learning patterns. Secondly, our work on social messaging groups characterizes the formation and evolution pattern of chat groups regarding their lifecycles, structures dynamics, and underlying diffusion processes. Finally, we design and deploy a large-scale online experiment to explore how social tie, as a type of incentive, can help call back dropout users in a social game community. To the end, studying engagement and incentive offers us an opportunity to understand the fundamental principles that drive our online behaviors and activities - from individuals, to groups, to communities - and, in this way, to help design and build better online communities and organizations.