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

这个跨学科的研究项目将建立和测试一个计算理论，描述影响在线社区成功的因素和过程。研究人员将在这个项目的过程中开发和测试新的理论，以便在多个分析层面上更好地预测社区的成功，包括成员的支持、社区维护、生产和关键利益相关者的利益。在这项研究中产生的计算理论将提供新的科学见解来解释现有在线社区中成功的变化，并提供新的工程见解，可用于改进设计选择。该项目将促进人们了解这些设计因素如何相互作用，影响在线社区的成功和成员体验。正如维基百科、大规模开放在线课程（MOOCs）、对等资助和借贷网站以及在线健康支持团体的流行所表明的那样，在线社区已经成为对等内容生产、信息交换、教育和社会互动的商业和社会重要平台。尽管一些社区取得了成功，但大多数新创建的社区未能生存下来，或者无法实现其目标，无法吸引成员或产生有价值的工件。即使在像维基百科或对等借贷网站Kiva这样成功的社区中，也有一些小组比其他小组更成功。失败的一个原因是缺乏建立和管理在线社区的循证指导，以及缺乏在实施之前预测设计和管理决策效果的技术。先前对在线社区成功的研究包括实证研究和专业理论，以解释社区成功的单个方面，如成员承诺或贡献。商业公司通常使用A/B测试在他们的社区中做出特定的设计选择。然而，很少有人尝试建立一个全面的、以证据为基础的理论来解释在线社区的属性和过程是如何相互作用的，从而决定了它们的成功。这些研究人员将使用基于代理的建模，这是一种计算机模拟技术，可以模拟个体社区成员在加入、贡献或离开社区时所做的决定。他们将利用并整合社会心理学、经济学、组织行为学和传播学的组成理论来模拟这些决策。他们将使用来自三个不同社区的经验数据——众筹团队、健康支持小组和STEM课程内的同伴支持论坛——来建立和测试该模型。一旦实证研究证实该模型可以解释当前存在的社区行为，该模型将用于探索如何通过改变子群体的规模和多样性、内容和联系推荐以及排行榜设计来改善社区。研究人员将使用虚拟实验和现场实验来验证该模型的预测。本项目由美国国家科学基金会跨学科行为与社会科学研究（IBSS）竞赛资助。

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.