Collaborative Research: Social Networking Tools to Enable Collaboration in the Tobacco Surveillance, Epidemiology, and Evaluation Network (TSEEN)

备受瞩目的美国癌症协会的报告指出,癌症已超过心脏病成为头号杀手85岁以下的成年人今天在美国。排在榜首的是肺癌，这是一种完全可以预防的疾病。应对癌症等公共卫生威胁是全系统的严峻挑战，将受益于以网络为中心的方法。例如，当烟草研究界意识到他们花了十多年的时间才发现他们已经收集了大量的经验证据，分布在其烟草研究人员网络中，表明“轻”（低焦油/低尼古丁品牌）香烟既不能减少患癌症的风险，也不能减少患癌症的风险时，研究人员开始理解在整个社区有效共享资源和信息的重要性。作为回应，参与公共卫生的政府机构在开发数字政府网络基础设施——烟草系统集成网格（TobacSIG）方面进行了大量基础投资，以实现烟草监测、流行病学和评估网络（TSEEN）内的合作。虽然这样一个潜在的网络基础设施是一个先决条件，但发现的延迟（如上面提到的“轻”香烟的致癌作用）促使TSEEN社区强调社会网络推荐工具的必要性，作为任何提高其合作系统效率的努力的关键组成部分。该项目将开发、部署和评估社会网络工具，以加强TSEEN成员之间使用TobacSIG网络基础设施的协作。拟议的项目汇集了信息科学、社会科学和公共卫生领域的研究人员，他们已经与政府伙伴建立了强有力的合作关系，以发展网络，支持公共卫生领域的跨学科研究。研究人员协助政府合作伙伴制定挑战和设想解决方案；因此，研究团队将充分利用美国国立卫生研究院国家癌症研究所及其合作政府机构在烟草sig网络基础设施方面投入的大量财政和人力资源。知识价值:该项目是一个开拓性的努力整合社交网络推荐工具作为协作系统的一个组成部分的上下文中数字政府。首先，提议的项目将扩展对涉及多维网络的协作网络结构出现的理论理解，其中节点可能是个人、文档、数据集、服务（如可视化分析工具）或关键词/概念。其次，它将开创网络转诊系统对合作结果影响的理论发展和测试。具体而言，拟议的项目将评估以下因素对协作结果的影响程度：(i)不同理论衍生的网络推荐结构，（ii）为网络推荐系统用户提供的不同激励结构，（iii）用于生成推荐的不同类型的网络数据，以及（iv）用于表示网络推荐的不同信息可视化。第三，该研究将扩展指数随机图建模技术，该技术已被广泛用于估计相对较小（通常不大于500）的一维网络的结构依赖性。拟议的项目将把这些技术扩展到包含超过10,000个节点的多维网络。更广泛的影响：随着网络基础设施被用于支持政府和其他地方的大型社区之间的协作，越来越明显的是，社交网络工具具有巨大的潜力。在这个项目中，研究人员将寻求回应“如果烟草监测流行病学评估网络知道它所知道的就好了”。将这一相同的叠句的相关性推广到广泛的其他背景，表明了所提议的研究的更广泛的影响。拟议研究的结果和成果将立即推广到社交网络转介工具的设计和部署，以支持公共卫生内外其他数字政府工作之间的协作。此外，这个项目的政府和非政府合作伙伴都有非常好的条件，可以将明智地使用网络推荐系统所提供的协作流畅性技能纳入他们定期安排的教育、培训和外展研讨会。最后，根据定义，社会网络转诊系统有可能增加在公共卫生界吸引更多不同成分的可能性（在性别、种族、年龄、资历、学科观点方面）。这一扩大的网络还将为这些社区内以前处于不利地位的成员提供指导机会。

A highly publicized report by the American Cancer Society noted that cancer has surpassed heart disease to become the number one killer of adults under the age of 85 in the United States today. At the top of the list was lung cancer, a disease that is eminently preventable. Addressing public health threats like cancer are acute, system-wide challenges that would benefit from network-centric approaches. For example, when the tobacco research community realized it had taken over a decade to discover that they had already collected substantial empirical evidence, distributed across its network of tobacco researchers, indicating that 'light' (low-tar/low-nicotine brands) cigarettes reduced neither exposure to nor risk of cancer, researchers began to understand the importance of effectively sharing resources and information across the entire community. In response, government agencies involved in public health have made a substantial foundational investment in developing a digital government cyberinfrastructure--Tobacco Systems integration Grid (TobacSIG)--to enable collaboration within the Tobacco Surveillance, Epidemiology, and Evaluation Network (TSEEN). While such an underlying cyberinfrastructure is a prerequisite, delays in discoveries (such as the carcinogenic effects of 'light' cigarette mentioned above) have prompted the TSEEN community to underscore the need for social network referral tools as a crucial component of any effort to enhance the efficacy of their collaboration system.This project will develop, deploy and assess social networking tools to enhance collaboration among members of TSEEN using the TobacSIG cyberinfrastructure. The proposed project brings together researchers in information science, social science, and public health who have established strong collaborations with government partners on the development of networks to support transdisciplinary research in public health. The researchers have assisted the government partners in formulating the challenges and envisioning solutions; hence the research team is will positioned to leverage the substantial financial and human resources being invested by NIH National Cancer Institute and its partner government agencies in the TobacSIG cyberinfrastructure.Intellectual Merit: The proposed project is a pioneering effort at incorporating social network referral tools as an integral part of collaborative systems within the context of digital government. First, the proposed project will extend theoretical understanding of the emergence of collaboration network structures involving multidimensional networks, where nodes may be individuals, documents, data sets, services (such as visual-analytic tools), or keywords/concepts. Second, it will pioneer theory development and testing about the influence of network referral systems on collaboration outcomes. Specifically, the proposed project will assess the extent to which collaboration outcomes are influenced by (i) different theoretically-derived structures of network referrals, (ii) different incentive structures provided to users of the network referral system, (iii) different types of network data used to generate referrals, and (iv) different information visualizations used to represent network referrals. Third, the research will extend the exponential random graph modeling techniques that have been largely used to estimate structural dependencies in relatively small (typically no larger than 500) one-dimensional networks. The proposed project will extend these techniques to multidimensional networks containing over 10,000 nodes.Broader Impacts: As cyberinfrastructure is deployed to support collaboration among large communities in government and elsewhere, it is increasingly obvious that social network tools have immense potential. In this project the researchers will seek to respond to the refrain 'if only the Tobacco Surveillance Epidemiology Evaluation Network knew what it knew.' Generalizing the relevance of this same refrain to a wide spectrum of other contexts is suggestive of the broader impacts of the proposed research. The findings and deliverables of the proposed research will be immediately generalizable to the design and deployment of social network referral tools to support collaboration among other digital government efforts within public health and beyond. Further, the government and non-government partners in this project are exceptionally well-equipped to incorporate into their regularly scheduled education, training, and outreach workshops the skill sets of collaborative fluency afforded by the judicious use of network referral systems. Finally, by definition, social network referral systems have the potential to increase the likelihood of drawing in more diverse constituents within the public health community (in terms of gender, ethnicity, age, seniority, disciplinary perspectives) than heretofore possible. This extended network will also offer opportunities for mentoring of previously disadvantaged members within these communities.