CHS: Medium: Collaborative Research: Understanding Online Creative Collaboration Over Multidimensional Networks

CHS：媒介：协作研究：理解多维网络上的在线创意协作

• 批准号: 1514283
• 负责人: Christoph Riedl
• 金额: $ 52.56万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1514283&HistoricalAwards=false
• 关键词: CHS; Medium; Collaborative; Research; Understanding

This is a study of the structure and dynamics of Internet-based collaboration. The project seeks groundbreaking insights into how multidimensional network configurations shape the success of value-creation processes within crowdsourcing systems and online communities. The research also offers new computational social science approaches to theorizing and researching the roles of social structure and influence within technology-mediated communication and cooperation processes. The findings will inform decisions of leaders interested in optimizing all forms of collaboration in fields such as open-source software development, academic projects, and business. System designers will be able to identify interpersonal dynamics and develop new features for opinion aggregation and effective collaboration. In addition, the research will inform managers on how best to use crowdsourcing solutions to support innovation and marketing strategies including peer-to-peer marketing to translate activity within online communities into sales.This research will analyze digital trace data that enable studies of population-level human interaction on an unprecedented scale. Understanding such interaction is crucial for anticipating impacts in our social, economic, and political lives as well as for system design. One site of such interaction is crowdsourcing systems - socio-technical systems through which online communities comprised of diverse and distributed individuals dynamically coordinate work and relationships. Many crowdsourcing systems not only generate creative content but also contain a rich community of collaboration and evaluation in which creators and adopters of creative content interact among themselves and with artifacts through overlapping relationships such as affiliation, communication, affinity, and purchasing. These relationships constitute multidimensional networks and create structures at multiple levels. Empirical studies have yet to examine how multidimensional networks in crowdsourcing enable effective large-scale collaboration. The data derive from two distinctly different sources, thus providing opportunities for comparison across a range of online creation-oriented communities. One is a crowdsourcing platform and ecommerce website for creative garment design, and the other is a platform for participants to create innovative designs based on scrap materials. This project will analyze both online community activity and offline purchasing behavior. The data provide a unique opportunity to understand overlapping structures of social interaction driving peer influence and opinion formation as well as the offline economic consequences of this online activity. This study contributes to the literature by (1) analyzing multidimensional network structures of interpersonal and socio-technical interactions within these socio-technical systems, (2) modeling how success feeds back into value-creation processes and facilitates learning, and (3) developing methods to predict the economic success of creative products generated in these contexts. The application and integration of various computational and statistical approaches will provide significant dividends to the broader scientific research community by contributing to the development of technical resources that can be extended to other forms of data-intensive inquiry. This includes documentation about best practices for integrating methods for classification and prediction; courses to train students to perform large-scale data analysis; and developing new theoretical approaches for understanding the multidimensional foundations of cyber-human systems.

这是对基于Internet的协作的结构和动态的研究。 该项目寻求开创性的见解，了解多维网络配置如何影响众包系统和在线社区中价值创造过程的成功。 该研究还提供了新的计算社会科学方法来理论化和研究社会结构的作用以及在技术介导的沟通和合作过程中的影响。 这些发现将为有兴趣优化开源软件开发，学术项目和业务等领域的所有形式的协作的领导者的决策提供信息。 系统设计师将能够识别人际动态，并为意见聚合和有效协作开发新功能。 此外，该研究将告知管理人员如何最好地使用众包解决方案来支持包括点对点营销在内的创新和营销策略，以将在线社区内的活动转化为销售。这项研究将分析数字痕量数据，以实现前所未有的规模研究人口水平的人类互动。 了解这种互动对于预期对我们的社会，经济和政治生活以及系统设计的影响至关重要。 这种互动的一个站点是众包系统 - 社会技术系统，在线社区通过该系统由不同的和分布式个人组成，可以动态地协调工作和关系。 许多众包系统不仅会产生创意内容，还包含了丰富的协作和评估社区，在这些协作和评估中，创造性内容的创作者和采用者之间以及通过重叠的关系（例如隶属关系，交流，亲和力和购买）进行了与文物相互作用。 这些关系构成了多维网络，并在多个层面上创建结构。 实证研究尚未研究众包中的多维网络如何实现有效的大规模合作。数据来自两个截然不同的来源，因此为在一系列以在线创造为导向的社区中提供了比较的机会。 一个是一个用于创意服装设计的众包平台和电子商务网站，另一个是参与者根据废料制作创新设计的平台。 该项目将分析在线社区活动和离线购买行为。数据提供了一个独特的机会，可以理解推动同伴影响力和意见形成的社会互动的重叠结构，以及这项在线活动的离线经济后果。 这项研究通过（1）分析这些社会技术系统中人际关系和社会技术相互作用的多维网络结构，（2）建模成功如何将成功反馈到价值创造过程中并促进学习的学习方式，并促进学习的方法，以及（3）开发在这些上下文中创造创意产品的经济成功的方法。 各种计算和统计方法的应用和集成将通过为技术资源的发展做出贡献，从而为更广泛的科学研究社区提供大量的红利，这些技术可以扩展到其他形式的数据密集型询问。 这包括有关整合分类和预测方法的最佳实践的文档；培训学生进行大规模数据分析的课程；并开发新的理论方法来理解网络人类系统的多维基础。