CHS: Small: Large-Scale Examination of the Impact of Shocks on Crowd Attributes and Performance in Collaborative Volunteering Systems

这个项目将促进对在线资源志愿者群体在面对与他们的工作相关的突发事件时的表现的理解。这些志愿者贡献者（统称为“人群”）提供有价值的资源，如维基百科文章、公民科学项目的数据和开源软件。然而，尽管之前有关于人群和情境的哪些方面会导致高质量资源的研究，但这些研究通常假设人群、群体，尤其是情境是相对稳定的。在实践中，情况经常会发生突然的变化，或“冲击”：名人或世界事件的死亡可能会影响与之相关的维基百科文章，而一个软件项目可能会发布一个新版本或发现一个严重的错误。在这个项目中，研究人员将使用存储在GitHub网站上的维基百科文章和开源项目的公开历史来分析人群对冲击的反应，以及它们如何影响他们创建的资源。为此，他们将使用个人和群体行为理论来衡量群体和他们所从事的资源的有意义的属性，然后使用数据分析技术来理解(1)群体在冲击期间如何变化；(2)面对冲击时，群体的哪些属性预示着高弹性和资源质量；(3)这些影响如何根据所经历的冲击类型而变化。从这项工作中获得的见解也将为管理软件和社区的人提供设计建议，使人群能够创造这些有社会价值的资源。这项工作将从构建人群及其产生的资源的特征开始。重点将放在维基百科先前的实证工作和组织理论的工作中提出的与协作众包系统的性能相关的特征上。对于群体，这些因素包括团队组成和参与行为，包括经验、多样性和工作平衡；关于创造资源的团队协调的数量和基调；并根据个体之间的沟通模式推断出合作者的网络结构。在资源的情况下，它包括属性，包括其内部和外部受欢迎程度，以及额定或估计的当前质量。研究人员将使用这些特征来分析群体在面对各种冲击时的表现，包括资源质量、工人状态和相关外部事件的变化。为此，研究人员将开发算法来检测人群经历冲击的时间，然后使用倾向得分匹配属性，如资源质量和人群规模，以找到没有经历冲击的相似人群的比较集。然后，他们将使用机器学习分类器和分段回归分析技术来分析冲击发生后资源周围人群的组成和行为变化，相对于人群在比较资源周围的行为。一旦这些模型被开发出来，团队将把它们应用于预测人群对冲击的预期弹性和协作系统内部潜在的冲击发生的问题。

This project will advance understanding of how groups of volunteer contributors to online resources perform in the face of sudden, unexpected events related to their work. These volunteer contributors (collectively, "crowds") produce valuable resources such as Wikipedia articles, data for citizen science projects, and open source software. However, though there has been prior research on what aspects of crowds and situations lead to high quality resources, such research normally assumes that the people, groups, and especially situations are relatively stable. In practice, situations often encounter sudden changes, or "shocks": the death of a celebrity or a world event can affect Wikipedia articles related to it, while a software project might release a new version or discover a critical bug. In this project, the investigators will use the public history of Wikipedia articles and open source projects stored on the GitHub website to analyze how crowds react to shocks, and how that affects the resources they create. To do this they will use theories of individual and group behavior to measure meaningful attributes of both the crowds and the resources they work on, then use data analysis techniques to understand (1) how crowds change during shocks; (2) what attributes of crowds predict high resilience and resource quality in the face of shocks; and (3) how these effects change depending on the type of shock that is experienced. The insights gained from the work will also lead to design recommendations for people who manage the software and communities that enable crowds to create these socially valuable resources.The work will start by constructing features of crowds and the resources they produce. The focus will be on features that prior empirical work in Wikipedia and work from organization theory suggest will be relevant to performance in collaborative crowdsourcing systems. For crowds, these include elements about team composition and participation behavior including experience, diversity, and work balance; about the amount and tone of team coordination around creating the resources; and about the inferred network structure of the collaborators based on individuals' communication patterns with each other. In the case of resources, it includes attributes including their internal and external popularity, and rated or estimated current quality. The investigators will use these features to analyze how the crowds perform when facing a variety of kinds of shocks, including changes in resource quality, worker status, and relevant external events. To do this, the investigators will develop algorithms to detect times when a crowd has experienced a shock, then use propensity score matching on attributes such as resource quality and crowd size to find comparison sets of similar crowds that have not experienced a shock. They will then use machine learning classifiers and segmented regression analysis techniques to analyze changes in the composition and behavior of the crowd around resources after shocks occur, relative to how the crowd behaves around the comparison resources. Once these models have been developed, the team will apply them to questions of predicting both the anticipated resilience of a crowd to a shock and the potential occurrence of shocks internal to the collaboration system.