NSCC/LA: Collaborative Research: Modeling Discourse and Social Dynamics in Authoritarian Regimes

NSCC/LA：合作研究：威权政权中的话语和社会动态建模

• 批准号: 0904913
• 负责人: David Beaver
• 金额: $ 34.97万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0904913&HistoricalAwards=false
• 关键词: NSCC; LA; Collaborative; Research; Modeling

The ways people use words can provide insights into their thoughts, motives, and relationships with others. Discourse patterns can be modeled to predict deception, status, group processes, emotional states, and personality. An important question is whether the relationship between discourse and social dynamics can be extended to create predictive models of social processes across multiple languages and cultures, and among military, political and civilian organizations in authoritarian regimes. The specific goals of the project are a) to define and compare the ways natural language reflects social dynamics through the analysis of a wide range of documents across languages and cultures, b) to develop annotated and socially indexed multi-language databases of communication (e.g., English, Arabic, Chinese, Spanish, and Korean), along with preliminary research tools that can enhance our ability to conduct research across languages and cultures, and c) to address key national security questions emerging from the collection of vast amounts of digitized documents and communication, such as the capability to understand past actions and cognitions of previous regimes and assess emerging threats. The project will focus on three types of social dynamics: a) Leadership, identity and group dynamics, and the degree to which the psychological and social identities of people and groups can be determined through writing and speech; b) Cohesion of text and social processes, and whether it is possible to identify the connections among people in a social group, such as the stability of the group and likelihood of defection, by comparing the ways the group members separately use words; and c) Deception and misinformation, and whether it is possible to identify deception and deceptive intent from language-based cues. A cross-cutting research theme is the complexities of conducting computational analysis of discourse and social dynamics across multiple languages and translated corpora. For example, to what degree does translation distort the link between language and social dynamics? The project will advance social science theory, developing new models linking language use with social dynamics and providing a new way of looking at large cross-sections of a society. New computational methods will be developed that can be applied to large, representative samples of text from across an entire country's political and cultural landscape. These methods, unlike traditional analytical methods, do not require individual analysis by highly trained social scientists and historians and can provide the capacity to process documents that may number in the millions. These developments and findings will advance analyses of social dynamics that were not previously possible and can assist scientists and practitioners, such as security analysts, in understanding information flow and social structure at a macroscopic level. Such knowledge could potentially help security analysts to pinpoint individuals and groups that merit direct study by trained experts.

人们使用语言的方式可以洞察他们的想法、动机以及与他人的关系。可以对话语模式进行建模来预测欺骗、地位、群体过程、情绪状态和个性。一个重要的问题是，话语与社会动态之间的关系是否可以扩展到创建跨多种语言和文化以及独裁政权的军事、政治和民间组织之间的社会过程的预测模型。该项目的具体目标是：a）通过分析跨语言和文化的广泛文档来定义和比较自然语言反映社会动态的方式，b）开发带注释和社会索引的多语言交流数据库（例如英语、阿拉伯语、中文、西班牙语和韩语），以及可以增强我们跨语言和文化进行研究能力的初步研究工具，c）解决关键的国家安全问题 来自大量数字化文件和通信的收集，例如了解前政权过去的行动和认知以及评估新出现的威胁的能力。该项目将重点关注三种类型的社会动态：a) 领导力、身份和群体动态，以及通过写作和演讲确定人和群体的心理和社会身份的程度； b) 文本和社会过程的衔接性，以及是否可以通过比较群体成员单独使用词语的方式来识别社会群体中人们之间的联系，例如群体的稳定性和叛逃的可能性； c) 欺骗和错误信息，以及是否可以从基于语言的线索中识别欺骗和欺骗意图。跨领域的研究主题是对多种语言和翻译语料库的话语和社会动态进行计算分析的复杂性。例如，翻译在多大程度上扭曲了语言和社会动态之间的联系？该项目将推进社会科学理论，开发将语言使用与社会动态联系起来的新模型，并提供一种看待社会各个方面的新方法。将开发新的计算方法，可应用于来自整个国家政治和文化景观的大型代表性文本样本。与传统的分析方法不同，这些方法不需要训练有素的社会科学家和历史学家进行单独分析，并且可以提供处理数百万份文档的能力。这些进展和发现将推进对社会动态的分析，这在以前是不可能的，并且可以帮助科学家和从业者（例如证券分析师）在宏观层面上理解信息流和社会结构。这些知识可能有助于安全分析师查明值得训练有素的专家直接研究的个人和团体。