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Automated Determination of the Veracity of Interview Statements from People of Interest to an Operational Security Force

机译:自动确定从业务安全部队的兴趣人群中采访陈述的真实性

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In deception detection research validity issues have been raised when subjects are used in controlled laboratory experiments. Studying real-life deception detection is a complicated endeavor because researchers do not have the control in field studies that exist in laboratory experiments so determining ground truth is challenging. This study reports the findings of the combination of some successful previous attempts at automated deception detection in computer-mediated communication results of a study of real-world data from an operation security force. Message feature mining is used to evaluate the effectiveness of technology as an aid to deception detection in actual stressful situations with unpleasant long term consequences. The study analyzes 18 statements (9 truthful, 9 deceptive) from a military service's investigative unit using message feature mining. The analysis resulted in a 72% rate of accuracy in correctly classifying the messages.
机译:在欺骗性检测中,在受控实验室实验中使用受试者时已经​​提出了研究有效性问题。研究现实生活欺骗性检测是一种复杂的努力,因为研究人员在实验室实验中存在的实地研究中没有控制,因此决定了基础真是挑战。本研究报告了在计算机介导的通信结果中从操作安全部队研究了实际数据的自动欺骗性检测的一些成功先前尝试的结合的结果。消息特色挖掘用于评估技术的有效性,以令人难以愉快的长期后果在实际压力情况下对欺骗性检测的辅助。该研究分析了使用消息特征挖掘的军事服务调查单位的18个陈述(9个真实,9个欺骗性)。该分析在正确分类消息时占72%的准确度。

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