SoCS: Supporting A Nation of Neighbors with Community Analysis Visualization Environment

以计算机为中介的公民参与正在成为解决与医疗保健、能源可持续发展、教育、环境保护和灾害应对等经济和社会问题相关的当代问题的一种解决方案。这项由美国国家科学基金会资助的研究项目由马里兰大学的Ben Shneiderman、Alan Neustadtl和Catherine Plaisant进行，他们将研究社区安全系统Nation of Neighborhood成功和失败的原因。结果将使干预措施能够将平衡转向更大的成功。这项研究的一个产品将是基于计算机的社区分析可视化环境(CAVE)，它将使社区管理者能够使用可视化分析工具包，通过识别社区参与计划的有效和无效组成部分来测量社区的脉搏，并将使研究人员能够比较大量的社区，以了解区分社区参与计划成功和失败的特征。该项目将测试四个阶段的读者到领导者框架--该框架假设参与从读者到贡献者到合作者再到领导者，进入后续阶段的参与者越来越少--通过研究社区管理者战略来应对增加参与以及外部事件、恶意攻击、有害谣言和心怀不满的成员造成的威胁破坏的实际挑战。此外，研究结果将对许多以计算机为媒介的公民参与系统产生普遍影响，例如为应对自然灾害(地震、有毒废物排放等)、医疗暴发(食物中毒、流感、流行病等)和人类威胁(恐怖分子、连环杀手、炸弹袭击者、纵火犯等)而设计的系统。为该项目开发的计算工具也将对研究社区参与网络的研究人员有用。这项研究还可能为其他类型的社交网络的运作提供有用的见解，并可能对医院和学区等信息由大量人共享的组织产生影响。

Computationally-mediated civic participation is emerging as a solution to contemporary problems associated with economic and social issues such as healthcare, energy sustainability, education, environmental protection, and disaster response. The NSF-funded research project conducted by Ben Shneiderman, Alan Neustadtl, and Catherine Plaisant at the University of Maryland will study reasons for successes and failures of the community safety system, Nation of Neighbors. The results will enable interventions to shift the balance towards increasing success. One product of the research will be a computer-based Community Analysis Visualization Environment (CAVE) that will enable community managers to use a visual analytic toolkit to take the pulse of their communities by identifying effective and ineffective components of the community participation program, and will enable researchers to compare large numbers of communities to understand the features that distinguish successful from failing community participation programs. The project will test the four-stage Reader-to-Leader Framework -- which assumes that participation moves from reader to contributor to collaborator to leader, with fewer and fewer participants moving into each subsequent stage -- by studying community manager strategies for coping with the practical challenge of increased participation as well as threatening disruptions caused by external events, malicious attacks, harmful rumors, and disaffected members. In addition the results will have general implications for many computationally-mediated civic participation systems such as those designed for coping with natural disasters (earthquakes, toxic waste discharges, etc.), medical outbreaks (food poisoning, flu, pandemics, etc.), and human threats (terrorists, serial killers, bombers, arsonists, etc.). The computational tools developed for the project will also be useful to researchers studying community participation networks. The research may also provide useful insights into the working of other types of social networks and might have implications for organizations where information is shared by large numbers of people, such as hospitals and school districts.