IBSS-L: Developing, Testing, and Designing from a Computational Theory of Online Communities

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

This interdisciplinary research project will build and test a computational theory describing the factors and processes that influence the success of online communities. The investigators will develop and test new theories during the course of this project in order to better predict community success at multiple levels of analysis, including members' support, community maintenance, production, and key stakeholder benefits. The computational theory produced in this research will provide new scientific insights to explain variations in success in existing online communities and new engineering insights that can be applied to improve design choices. The project will advance knowledge of how these design factors interact to affect community success and member experiences in online communities. As the popularity of Wikipedia, Massively Open Online Courses (MOOCs), peer funding and lending sites, and online health support groups demonstrates, online communities have become commercially and societally important platforms for peer content production, information exchange, education, and social interaction. Although some communities succeed, the majority of newly created ones fail to survive or to achieve their goals to involve members or produce valuable artifacts. Even within a successful community like Wikipedia or the peer lending site Kiva, some subgroups are more successful than others. One reason for failure is the lack of evidence-based guidance for building and managing online communities as well as the paucity of techniques to predict the effects of design and management decisions before implementation. Prior research on online community success consists of empirical studies and specialized theories to explain single facets of community success, such as membership commitment or contribution. Commercial firms routinely use A/B testing to make specific design choices in their communities. There have been few attempts, however, to build a comprehensive, evidence-based theory to explain how online communities' attributes and processes interplay to determine their success. This investigators will use agent-based modeling, a computer simulation technique that models the decisions individual community members make in joining, contributing to, or leaving the community. They will draw on and integrate component theories from social psychology, economics, organizational behavior, and communications to model these decisions. They will use empirical data from three distinct communities -- crowd-lending teams, health support groups, and peer support forums within STEM classes -- to ground and test the model. Once empirical research has verified that the model can account for behavior in the communities as they currently exist, the model will be used to explore how to improve communities by instituting changes with respect to the size and diversity of subgroups, content and connections recommendations, and leaderboard design. The investigators will use virtual and field experiments to test predictions from the model. This project is supported through the NSF Interdisciplinary Behavioral and Social Sciences Research (IBSS) competition.