EAGER: Collaborative: Language-Action Causal Graphs for Trustworthiness Attribution in Computer-Mediated Communication

EAGER：协作：计算机介导的通信中可信度归因的语言-动作因果图

• 批准号: 1347120
• 负责人: Jeffrey Hancock
• 金额: $ 8万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1347120&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Language; Action; Causal

This collaborative research between Florida State University and Cornell University is to identify language-action features from text-based messages that can be used to dynamically infer a social actor's perceived trustworthiness. The team will investigate using optimal analysis techniques to calibrate trustworthiness reasoning, which can be used to computationally model actors' deceptive behaviors in cyber space and to infer actors' intent based on their words and actions.This research will have a transformative impact in understanding the dynamics of trusting relationships through observing language-action features and psychosocial trustworthiness attribution mechanisms. This study serves as a precursor to a socio-technical schema that will facilitate national security and data protection for the general populace while also protecting the individual's right to privacy. This study will contribute to the science of cyber-security, and will help the cyber-security community to understand and enable trustworthy communication and collaborative information behavior among computer-mediated groups in a systematic way.

佛罗里达州立大学和康奈尔大学的这项合作研究是为了从基于文本的信息中识别语言-行为特征，这些特征可以用来动态地推断社会行为者的可信赖性。该团队将研究使用最优分析技术来校准可信度推理，这可用于计算模拟参与者在网络空间中的欺骗行为，并根据参与者的言行推断他们的意图。本研究将通过观察语言-行为特征和社会心理信任归因机制，对理解信任关系的动态变化产生变革性影响。这项研究是社会技术模式的先驱，该模式将促进国家安全和普通民众的数据保护，同时也保护个人的隐私权。本研究将有助于网络安全科学的发展，并有助于网络安全界系统地理解和实现计算机媒介群体之间的可信通信和协作信息行为。