CAREER: Social and Economic Consequences of Information Diffusion in Networks

这个项目是一个三管齐下的研究和教育计划，专注于网络中的信息--信息的分发、传播、价值以及对社会和经济成果的影响。首先，该项目将绘制真实组织中随时间推移的信息流，并将生产经济学理论与社会网络理论相结合，以评估信息传播对个人和团队生产率的影响。这项工作将推动有关信息工作者生产和生产力的经济学理论；关于信息在社交网络中移动的社会学理论；分析数字网络中的信息内容同时保护隐私的技术方法；以及确定网络结构和生产力之间因果关系的统计方法。其次，研究将利用在线社交网络上的数据来确定对产品采用和需求中的同行影响的估计，并将开发和应用新的统计技术，将影响与选择、同质性和混杂因素分开，以优化有针对性的对等信息传播。第三，研究将开发一类基于网络的行为变化在社会网络中扩散的一般模型，并使用来自五个海量网络数据集的社会关系和健康相关行为的经验数据来参数化和验证这些模型。该项目的中心目标是：1)估计和提高信息工作者的生产力；2)建模、测量和改进点对点病毒营销和需求估计；3)改进与健康相关的预防和干预策略。拟议的活动旨在对科学、教育和社会产生更广泛的影响。我们对信息工作者生产力的了解是我们未来经济增长的基石，因此也是我们的繁荣和福利的基石。该项目还旨在改进对行为和结果的干预和预防战略，如疾病预防、肥胖和药物使用。开发可靠、可扩展的统计方法，以确定疾病、肥胖、吸烟和犯罪传播的因果关系，可以改变社会政策，以确保卫生干预措施具有适当的针对性。开发处理大型动态联网数据集的技术基础设施、保护隐私的文本分析以及估计信息在网络中传播的影响的统计方法，对电信、流行病学、医学、社会政策以及任何以网络外部性和本地网络效应为特征的行业或社会领域都有影响。在教育方面，该项目将在课堂上利用研究计划来强调网络和信息对商业战略、社会和健康的重要性。研讨会将围绕与网络中的信息传播有关的主题，在学术界和工业界之间建立一个新的合作研究社区。

This project is a three-pronged program of research and education focused on information in networks - its distribution, diffusion, value, and consequences for social and economic outcomes. First, this project will map the flow of information in real organizations over time and will combine economic theories of production with social network theory to estimate the effects of information diffusion on the productivity of individuals and teams. This work will advance economic theory concerning information worker production and productivity; sociological theory concerning the movement of information in social networks; technical methods for analyzing information content in digital networks while preserving privacy; and statistical methods to identify causal relationships between network structure and productivity. Second, the research will utilize data on online social networks to identify estimates of peer influence in product adoption and demand, and it will develop and apply new statistical techniques to separate influence from selection, homophily and confounding factors to optimize targeted peer-to-peer information diffusion. Third, the research will develop a general class of network-based models of the diffusion of behavior change in social networks, and parameterize and validate these models using empirical data on social relationships and health related behaviors from five massive networked datasets.The central goals of the project are: 1) to estimate and enhance the productivity of information workers, 2) to model, measure and improve peer-to-peer viral marketing and demand estimation, and 3) to improve health-related prevention and intervention strategies. The proposed activities are designed to have broader impact for science, education and society. Our understanding of information worker productivity is a cornerstone of our future economic growth and thus our prosperity and welfare. The project also aims to improve intervention and prevention strategies for behaviors and outcomes such as disease prevention, obesity, and drug use. Developing reliable, scalable statistical methods for establishing causality in the diffusion of disease, obesity, smoking and delinquency can change social policy to ensure health interventions are appropriately targeted. Development of technical infrastructures to deal with large dynamic networked datasets, privacy preserving text analysis, and statistical methods for estimating the impacts of information diffusion in networks have implications for telecommunications, epidemiology, medicine, social policy and any industry or social sphere characterized by network externalities and local network effects. Educationally, the project will utilize the research program in classes to highlight the importance of networks and information for business strategy, society and health. Workshops will develop a new collaborative research community across academia and industry around topics related to information diffusion in networks.