Collaborative Research: Interactive Deception and its Detection through Multi-modal Analysis of Interviewer-Interviewee Dynamics

欺骗是社会生活中普遍存在的一种特征，但由于欺骗者利用了人际交流过程的特征，因此往往没有被发现。他们在互动过程中调整自己的语言和非语言行为，以避免被发现。为了揭示使成功的欺骗或检测成为可能的沟通过程的复杂性和动力学，一个由来自亚利桑那大学、芝加哥大学、密歇根州立大学和罗格斯大学的传播学、语言学、心理学、计算机科学和管理信息系统研究人员组成的多学科团队将进行合作研究。来自科隆大学、伦敦帝国理工学院和冰岛大学的国际专家将加入他们的行列。该项目将开发一个人际欺骗的理论模型，将重点从稳定的个人行为转移到动态的互动模式。它将通过测量视频记录的人际交流实验中的语言和非语言特征来创建五个试验台。这五个实验分别是作弊实验、模拟盗窃实验、欺骗性访谈、小组协作任务和叙述任务。测量将包括对视觉、声音和语言特征的广泛自动化和人工注释测量。作为注释工作的一部分，该团队将改进和验证用于自动测量非语言和语言特征的软件。理论驱动的假设测试和探索性测试将对测量的沟通行为进行，以使用时间序列、基于贝叶斯的主题分析和人工智能数据挖掘技术来识别动态适应模式。该项目将通过形成同类最大的国际和多学科欺骗专家研究团队，整合多学科的知识和方法，完善对人类交流的计算机辅助分析，在自动化欺骗检测方面取得进展，并通过实验室交流培养新的调查人员，从而推进研究欺骗的科学基础设施。社会将受益于更有效的欺骗图景，可以指导公共、商业、政府和安全环境中的培训和侦查工作。

Deception is a pervasive feature of social life yet often goes undetected because deceivers capitalize on features of the interpersonal communication process. They adjust their verbal and nonverbal behavior over the course of an interaction in ways that evade detection. To uncover the complexities and dynamics of the communication processes that make successful deception or detection possible, collaborative research will be conducted by a multidisciplinary team of communication, linguistics, psychology, computer science, and management information systems researchers from the University of Arizona, University of Chicago, Michigan State University, and Rutgers University. They will be joined by international experts from the University of Cologne, Imperial College London, and University of Iceland. The project will develop a theoretical model of interpersonal deception that shifts emphasis from stable individual behaviors to dynamic interaction patterns. It will create five test beds by measuring the verbal and nonverbal features from video-recorded interpersonal communication experiments. The five experiments are a cheating experiment, a mock theft experiment, deceptive interviews, a group collaboration task, and a narration task. Measurement will consist of extensive automated and human-annotated measurement of visual, vocal, and verbal features. As part of the annotation work, the team will refine and validate software for automated measurement of nonverbal and verbal features. Theory-driven hypothesis tests and exploratory tests will be conducted on the measured communication behaviors to identify dynamic adaptation patterns using time-series, Bayesian-based Theme analyses, and artificial intelligence data mining techniques. The project will advance the scientific infrastructure for studying deception by forming the largest international and multidisciplinary research team of deception experts of its kind, integrating the knowledge and methods of multiple disciplines, refining computer-aided analysis of human communication, making progress in automating deception detection, and educating new investigators through laboratory exchanges. Society will benefit from a more valid picture of deception that can guide training and detection efforts in public, business, government, and security settings.