Social production communities can be powerful engines for harnessing the efforts of many individuals to produce valuable artifacts and knowledge. However, their success critically depends on members' ability to effectively contribute. As the size and complexity of the community grows, so do challenges to members' understanding of the content and collaboration. These challenges decrease the ability of the team to work together, and the quality of the work product.
The researchers propose partnering humans with intelligent interfaces that improve contribution effectiveness. They will create intelligent algorithms and interfaces that go beyond supporting people simply foraging for information to information farming, in which members of the community work together to plant the seeds of the information the community needs, nurture the growth of those seeds into valuable information, and weed out the information that detracts from the value of the farm.
The research is based on theories of human information processing, and will extend those theories to environments in which people are producing information. The researchers will explore new algorithms and interfaces based on the extended theories, and will carry out studies to understand how the theories work in practice.
Social production communities are economically and socially important. Open source software runs large parts of our economy; Wikipedia is revolutionizing knowledge production and consumption, providing free access to one of the largest bodies of knowledge gathered in human history. The proposed research will directly improve Wikipedia, and will contribute to understanding how social production communities work.
