Collaborative Research: SGER: Computer-Assisted Interpretation of Citizen Input in Rebuilding Lower Manhattan

合作研究：SGER：重建曼哈顿下城时公民意见的计算机辅助解释

• 批准号: 0439096
• 负责人: David Stark
• 金额: $ 3.46万
• 依托单位: Columbia University
• 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: 2004-09-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0439096&HistoricalAwards=false
• 关键词: Collaborative; Research; SGER; Computer; Assisted

This is a collaborative grant with two PIs; Javed Mostafa of Indiana, and David Stark of Columbia. Intellectual MeritWith a grant from the NSF Digital Government Program, David Stark has been studying the role of information technologies in the public debate surrounding the rebuilding of Lower Manhattan in the wake of the September 11 attacks on the World Trade Center. In the process of conducting that research Stark's team has assembled an extensive digital archive containing 5,000 participant oral statements from one town hall meeting and an additional 19,000 oral statements collected at 240 different venues around New York City in the 'Imagine New York Envisioning Workshops'. These gathered statements provide a rich opportunity for testing various strategies of computer-assisted interpretation because they provide an opportunity to compare the conceptual patterns discerned by human intelligence with findings reached through the analytical methods of artificial intelligence. Supporting the initial explotation of that archive is the purpose of this grant.The technical component of this grant arises from work Javed Mostafa has done under an NSF ITR grant. Data mining research concentrating on spontaneous human conversations is at an early stage of development. Mostafa's approach to data mining can offer different ways to analyze the same data. The project has three specific goals: 1) to detect emergent concepts by applying techniques that do not impose any a priori conditions; 2) to use techniques for analyzing known concepts by applying constraints on the mining process, and 3) to develop visualization of the results to facilitate interpretation by social scientists and support direct validation by citizen participants. Broad Impact Computer mediated communication offers new channels for citizens to express their views to elected officials and government agencies. Often, the resulting deluge of comments poses a technical and political challenge. How can officials/agencies make sense of large-scale citizen input? How can meaningful patterns be efficiently and effectively identified? This project will contribute to advancing understanding of the opportunities and the limitations of computer-assisted interpretation. Its findings will be of considerable interest to scholars as well as to government managers responsible for the rebuilding of lower Manhattan. Summary Many challenges involved in creating new data mining tools demands an interdisciplinary collaboration for access to new data; this project offers such an opportunity. This time-critical testing of artificial intelligence methods will be important in understanding the public input to rebuilding lower Manhattan.

这是与两位 PI 的合作资助；印第安纳州的贾韦德·穆斯塔法和哥伦比亚的大卫·斯塔克。 智力优势 在 NSF 数字政府计划的资助下，David Stark 一直在研究信息技术在 9 月 11 日世贸中心袭击事件后围绕曼哈顿下城重建的公开辩论中的作用。在进行这项研究的过程中，斯塔克的团队收集了一份内容广泛的数字档案，其中包含来自一次市政厅会议的 5,000 份参与者口头陈述，以及在纽约市 240 个不同地点的“想象纽约展望研讨会”中收集的另外 19,000 份口头陈述。这些收集到的陈述为测试计算机辅助解释的各种策略提供了丰富的机会，因为它们提供了将人类智能识别的概念模式与通过人工智能分析方法得出的发现进行比较的机会。支持对该档案的初步利用是这笔赠款的目的。这笔赠款的技术部分来自 Javed Mostafa 在 NSF ITR 赠款下所做的工作。专注于人类自发对话的数据挖掘研究尚处于发展的早期阶段。 Mostafa 的数据挖掘方法可以提供分析相同数据的不同方法。该项目有三个具体目标：1）通过应用不强加任何先验条件的技术来检测新兴概念； 2）通过对挖掘过程施加约束来使用分析已知概念的技术，以及3）开发结果的可视化，以促进社会科学家的解释并支持公民参与者的直接验证。影响广泛 以计算机为媒介的交流为公民向民选官员和政府机构表达观点提供了新的渠道。通常，由此产生的大量评论会带来技术和政治挑战。官员/机构如何理解大规模公民的意见？如何高效且有效地识别有意义的模式？该项目将有助于加深对计算机辅助口译的机会和局限性的理解。其研究结果将引起学者以及负责曼哈顿下城重建的政府管理者的极大兴趣。总结 创建新的数据挖掘工具涉及的许多挑战需要跨学科协作来访问新数据；这个项目提供了这样的机会。这种对人工智能方法的时间紧迫的测试对于了解公众对重建曼哈顿下城的意见非常重要。