Peer production sites such as Wikipedia, Citizen Science, and even e-learning platforms depend critically on maintaining the engagement of their participants. The vast majority of users in such systems exhibit casual and non-committed participation patterns, making very few contributions before dropping out and never returning to the system. We present a methodology for extending engagement and productivity in such systems by combining machine learning with intervention strategies (whether automated or induced by a human overseer). We demonstrate the efficacy of this approach on two real world problems: How to support student group-learning in the classroom, and how to increase the contributions of thousands of volunteers in one of the largest citizen science platforms on the web.
Info on Kobi Gal Ben: http://www.eecs.harvard.edu/~gal/