DHB: Scalable Computational Analysis of the Diffusion of Technological Concepts

DHB：技术概念扩散的可扩展计算分析

• 批准号: 0729459
• 负责人: Ping Wang
• 金额: --
• 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: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0729459&HistoricalAwards=false
• 关键词: DHB; Scalable; Computational; Analysis; Diffusion

In any technology field, there is always some set of core concepts that dominate the agenda for research and practice. As a field progresses that set evolves, with new concepts replacing old ones. This project will model the dynamic social system through which technological concepts come to be perceived and understood. The primary research question is: How do the actions and opinions of individual actors give rise to more globally accepted concepts in a technology field, and how do such micro-macro dynamics change over time? That question will be answered by using an iterative process in which computational analysis of text is used to populate a model of salient aspects of social dynamics. Interpretations based on that model will then be used to guide refinement and enrichment of the computational analysis. This "computationally-supported case study" process offers a promising new approach to building and testing theories for social science research. By coupling focused extraction and classification for high-volume multi-source data with a multi-concept computational analysis strategy the project will create a new middle ground between today's richly analyzed but narrowly focused case studies and the presently available scalable but relatively shallow techniques, such as citation analysis. The project will focus on Information Technology as an exemplar field, leveraging the broad and accessible discourse on that topic found in vast collections of formal and informal sources. Text analytic techniques from information retrieval and computational linguistics will be adapted to detect specific concepts, and to connect those concepts with the people who write about them and with attitudes that those people express. An early goal will be to explain the extent to which the popularity of concepts results from social actors' actions and opinions. Contributions of this research are expected to include a scalable analytical framework that can affordably be extended to a broad range of other technologies, and a deeper understanding of the types of leverage that can be obtained from emerging text analytic techniques to enable new approaches to social science research.

在任何技术领域，总有一些核心概念主导着研究和实践的议程。随着一个领域的进步，这种集合也在演变，新概念取代了旧概念。这个项目将对动态的社会系统进行建模，通过这个系统，人们可以感知和理解技术概念。主要的研究问题是：单个参与者的行动和意见如何在技术领域产生更多全球接受的概念，以及这种微观-宏观动态如何随着时间的推移而变化？这个问题将通过使用迭代过程来回答，在该过程中，对文本的计算分析被用来填充社会动态的显著方面的模型。然后将使用基于该模型的解释来指导计算分析的改进和丰富。这一“计算支持的案例研究”过程为社会科学研究提供了一种构建和检验理论的很有前途的新方法。通过将对大量多源数据的集中提取和分类与多概念计算分析战略相结合，该项目将在今天分析丰富但关注范围狭窄的案例研究和目前可用的可扩展但相对较浅的技术(如引文分析)之间创造一个新的中间地带。该项目将侧重于将信息技术作为示范领域，利用在大量正式和非正式资料中找到的关于这一专题的广泛和可获得的论述。来自信息检索和计算语言学的文本分析技术将被用来检测特定的概念，并将这些概念与撰写这些概念的人以及这些人所表达的态度联系起来。早期的目标将是解释概念的受欢迎程度在多大程度上是由社会参与者的行为和观点造成的。预计这项研究的贡献将包括一个可扩展的分析框架，该框架可以以负担得起的价格扩展到广泛的其他技术，以及更深入地了解可以从新兴的文本分析技术中获得的杠杆类型，以实现社会科学研究的新方法。