Collaborative Research:Creating Dynamic Social Network Models from Sensor Data

协作研究：从传感器数据创建动态社交网络模型

• 批准号: 0433012
• 负责人: James Rehg
• 金额: $ 16.05万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-10-01 至 2007-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0433012&HistoricalAwards=false
• 关键词: Collaborative; Research; Creating; Dynamic; Social

This project will develop a hybrid method for rigorously observing structures of social interaction over time, and validate this method by comparison with conventional survey and observation designs. It will use both wearable and fixed computer devices to collect streaming data on research participants' physical location, speech, and motion, and then will develop computational models to infer structures of social interaction from these data. This suite of tools will thus allow direct automated measurement of networks of face-to-face interaction over time. Having demonstrated and validated this approach, the project will illuminate a set of classic theoretical problems that have eluded rigorous analysis under conventional methods. Substantial advances in modeling the dynamics of social networks have been frustrated by the paucity of appropriate data for empirical investigation, as scholars must often address dynamic theories using cross-sectional or sparse panel data. The team of investigators includes experts from both Computer Science and Sociology, integrates tools from both fields, and addresses questions that would be intractable without this interdisciplinary lens. For example, the precise measurement of interaction in time and space allows researchers to observe the co-evolution of social roles (as performed by individuals in day-to-day interaction) and structural positions in a social network. The streaming measures of social interaction allow a detailed analysis of conversations, analyzing how styles of communication change within social relationships over time, including the effect of structural position on styles of interaction and the effect of interaction style on position in the network. The research will examine the simple evolution of social networks over short (weeks) and long (month and/or years) time scales. Using Global Positioning Systems and various other location sensor technologies, the work will contribute an explicitly spatial investigation of network dynamics, modeling the interplay of the physical environment and social networks. For example, particular locations may serve as hubs or bridges, connecting otherwise disparate network components. Results may refine scientific understanding of the co-evolution of social networks and physical locations. The project will develop a set of methods for social network observation and analysis, generate datasets of unprecedented breadth and depth, and provide an independent standard for comparison of conventional tools, all of which will be invaluable resources for the broader scientific community. The resulting longitudinal network datasets are likely to be mined for insights into social network dynamics by many other researchers, while the team of graduate students working under this project will benefit from unique interdisciplinary training. Beyond basic research, the novel application of sensor-based and machine learning methods to understanding human communication has broad applicability to real-world social problems. As a simple example, a refined understanding of the co-evolution of networks and physical locations may provide insight into macro-level processes of community integration and disintegration, informing social architects and urban planners. The project will promote teaching, training and understanding among researchers in computer science and social science.

该项目将开发一种混合方法，用于随着时间的推移严格观察社会互动的结构，并通过与传统调查和观察设计的比较来验证该方法。它将使用可穿戴和固定计算机设备收集研究参与者的物理位置、语音和运动的流媒体数据，然后将开发计算模型，从这些数据推断社交结构。因此，这套工具将允许随着时间的推移直接自动测量面对面互动的网络。在演示和验证了这种方法之后，该项目将阐明一组在传统方法下无法进行严格分析的经典理论问题。由于缺乏用于实证研究的适当数据，社交网络动力学建模方面的实质性进展一直受挫，因为学者们经常必须使用横截面或稀疏面板数据来处理动态理论。调查团队包括来自计算机科学和社会学的专家，整合了这两个领域的工具，并解决了如果没有这种跨学科的视角将是难以解决的问题。例如，对时间和空间互动的精确测量使研究人员能够观察社会角色(如个人在日常互动中所执行的)和社交网络中的结构位置的共同进化。社交互动的流动测量方法可以对对话进行详细的分析，分析社会关系中的沟通风格如何随着时间的推移而变化，包括结构位置对互动风格的影响，以及互动风格对网络地位的影响。这项研究将考察社交网络在短(周)和长(月/或年)时间尺度上的简单演变。利用全球定位系统和各种其他位置传感器技术，这项工作将对网络动态进行明确的空间调查，模拟物理环境和社会网络的相互作用。例如，特定位置可以充当集线器或桥梁，连接原本完全不同的网络组件。结果可能会完善对社会网络和物理位置共同进化的科学理解。该项目将开发一套用于社会网络观察和分析的方法，生成前所未有的广度和深度的数据集，并为传统工具的比较提供一个独立的标准，所有这些都将成为更广泛的科学界的宝贵资源。由此产生的纵向网络数据集可能会被许多其他研究人员挖掘以深入了解社交网络动态，而在该项目下工作的研究生团队将受益于独特的跨学科培训。除了基础研究，基于传感器和机器学习方法在理解人类交流方面的新应用对现实世界的社会问题具有广泛的适用性。作为一个简单的例子，对网络和物理位置的共同进化的精细理解可能会提供对社区整合和解体的宏观过程的洞察，为社会建筑师和城市规划者提供信息。该项目将促进计算机科学和社会科学研究人员之间的教学、培训和理解。