Agent-Based Modeling to Inform Online Community Design: Impact of Topical Breadth, Message Volume, and Discussion Moderation on Member Commitment and Contribution

The design of complex social systems, such as online communities, requires the consideration of many parameters, a practice at odds with social science research that focuses on the effects of a small set of variables. In this paper, we examine three design decisions—topical breadth, message volume, and discussion moderation—and the trade-offs involved in making these decision. We show how synthesizing insights from multiple narrowly focused social science theories in an agentbased model helps understand factors that lead to the success of online communities. The agentbased model combines insights from theories related to collective effort, information overload, social identity, and interpersonal attraction to predict motivations for online community participation. We simulate three levels of topical breadth and message volume, and three types of moderation: 1) no moderation, in which all members are exposed to all messages; 2) community-level moderation, in which off-topic messages are deleted for everyone in the group; and 3) personalized moderation, in which people see a subset of messages matching their interests. Simulation experiments show higher member commitment in communities with broad topics and high message volume. Personalized moderation outperforms other types of moderation in increasing members’ commitment and contribution, especially in topically broad communities and those with high message volume. In comparison, community-level moderation increases commitment but not contribution, and only in topically narrow communities. Our results reveal a critical trade-off between informational and relational benefits. This research contributes a contingency view of online community design and theory-based guidelines. It also demonstrates the value of agent-based modeling in synthesizing simple social science theories to describe and prescribe behaviors in a complex system, generating novel insights that inform the design of online communities.