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描述影响在线社区成功的因素和过程的计算理论。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。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的成功差异，以及可以应用于改进设计选择的新的工程学见解。该项目将促进对这些设计因素如何相互作用影响社区成功和在线社区成员体验的了解。正如维基百科、大规模开放在线课程(MOOC)、同行融资和贷款网站以及在线健康支持小组的流行所表明的那样，在线社区已经成为同行内容生产、信息交换、教育和社交的重要商业和社会平台。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，也未能实现让成员参与或生产有价值的文物的目标。即使在像维基百科或同业借贷网站Kiva这样的成功社区里，一些小组也比其他小组更成功。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的影响的技术。以前关于在线社区成功的研究包括实证研究和专门的理论来解释社区成功的单一方面，如成员承诺或贡献。商业公司经常使用A/B测试来在他们的社区做出具体的设计选择。然而，很少有人尝试建立一个全面的、基于证据的理论来解释在线社区的属性和过程是如何相互作用来决定其成功的。调查人员将使用基于代理的建模，这是一种计算机模拟技术，模拟个体社区成员在加入、贡献或离开社区时做出的决定。他们将借鉴和整合社会心理学、经济学、组织行为学和传播学的组成部分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--群众借贷团队、健康支持小组和STEM课程中的同行支持论坛--来对该模型进行地面和测试。一旦实证研究证实该模型可以解释社区中现有的行为，该模型将被用来探索如何通过对子群体的大小和多样性、内容和连接建议以及排行榜设计进行改变来改善社区。研究人员将使用虚拟和现场实验来测试该模型的预测。该项目通过NSF跨学科行为和社会科学研究(IBSS)竞赛得到支持。

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.