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

SoCS：通过社区分析可视化环境支持邻国

• 批准号: 0968521
• 负责人: Ben Shneiderman
• 金额: $ 24.99万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0968521&HistoricalAwards=false
• 关键词: SoCS; Supporting; Nation; Neighbors; Community

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.

计算机介导的公民参与正在成为解决与医疗保健、能源可持续性、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。 由马里兰州大学的Ben Shneiderman，Alan Neustadtl和Catherine Plaisant资助的研究项目将研究社区安全系统Nation of Neighbors的成功和失败的原因。其结果将使干预措施能够改变平衡，以增加成功。 该研究的一个产品将是一个基于计算机的社区分析可视化环境（CAVE），它将使社区管理人员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件来把握社区的脉搏，并使研究人员能够比较大量的社区，以了解区分成功和失败的社区参与计划的特征。 该项目将测试四阶段读者到领导者框架-假设参与从读者到贡献者到合作者到领导者，越来越少的参与者进入每个后续阶段-通过研究社区管理者策略来应对增加参与的实际挑战以及外部事件，恶意攻击，有害谣言，和不满的成员。 此外，这些结果将对许多以计算为媒介的公民参与系统产生普遍影响，例如那些为应对自然灾害（地震，有毒废物排放等）而设计的系统，医疗突发事件（食物中毒、流感、流行病等），和人类威胁（恐怖分子、连环杀手、炸弹手、纵火犯等）。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工作提供有用的见解，并可能对医院和学区等大量人员共享信息的组织产生影响。