Current automatic deception detection approaches tend to rely on cues that are based either on specific lexical items or on linguistically abstract features that are not necessarily motivated by the psychology of deception. Notably, while approaches relying on such features can do well when the content domain is similar for training and testing, they suffer when content changes occur. We investigate new linguistically defined features that aim to capture specific details, a psychologically motivated aspect of truthful versus deceptive language that may be diagnostic across content domains. To ascertain the potential utility of these features, we evaluate them on data sets representing a broad sample of deceptive language, including hotel reviews, opinions about emotionally charged topics, and answers to job interview questions. We additionally evaluate these features as part of a deception detection classifier. We find that these linguistically defined specific detail features are most useful for cross-domain deception detection when the training data differ significantly in content from the test data, and particularly benefit classification accuracy on deceptive documents. We discuss implications of our results for general-purpose approaches to deception detection.

中文翻译：

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使用语言定义的特定细节来检测跨域的欺骗

当前的自动欺骗检测方法往往依赖于基于特定词汇项目或语言抽象特征的线索，这些线索不一定是由欺骗心理驱动的。值得注意的是，虽然依赖这些特征的方法在训练和测试的内容域相似时可以做得很好，但当内容发生变化时它们会受到影响。我们研究旨在捕捉特定细节的新的语言定义特征，这是真实与欺骗性语言的心理动机方面，可能是跨内容域的诊断。为了确定这些功能的潜在效用，我们在代表广泛的欺骗性语言样本的数据集上对它们进行了评估，包括酒店评论、关于情绪激动的话题的意见以及求职面试问题的答案。我们还评估这些特征作为欺骗检测分类器的一部分。我们发现，当训练数据与测试数据在内容上存在显着差异时，这些语言定义的特定细节特征对于跨域欺骗检测最有用，并且特别有利于欺骗性文档的分类准确性。我们讨论了我们的结果对欺骗检测的通用方法的影响。