Lying Through One’s Teeth: A Study on Verbal Leakage Cues (2021)

Abstract: Although many studies use the LIWC lexicon to show the existence of verbal leakage cues in lie detection datasets, none mention how verbal leakage cues are influenced by means of data collection, or the impact thereof on the performance of models. In this paper, we study verbal leakage cues to understand the effect of the data construction method on their significance, and examine the relationship between such cues and models’ validity. The LIWC word-category dominance scores of seven lie detection datasets are used to show that audio statements and lie-based annotations indicate a greater number of strong verbal leakage cue categories. Moreover, we evaluate the validity of state-of-the-art lie detection models with cross- and in-dataset testing. Results show that in both types of testing, models trained on a dataset with more strong verbal leakage cue categories—as opposed to only a greater number of strong cues—yield superior results, suggesting that verbal leakage cues are a key factor for selecting lie detection datasets.

## introduction one theory of lie detection is about cues to lying: why and when do liars and truth-tellers display different behavior? @xcite proposed two categories of cues: deception cues and leakage cues. deception cues relate to the content of lies, such as an inconsistency in one's story; leakage cues appear because liars' emotions betray their true feeling, which can be further classified into non-verbal and verbal leakage cues. @xcite reject the utility of focusing on liars' emotions but link such cues to cognitive load, supported by @xcite @xcite . @xcite analyzes 158 cues to deception, including non-verbal and verbal leakage cues, finding that verbal leakage cues are more reliable than others. studies such as @xcite , @xcite , and @xcite show that verbal leak-age cues can be found through psycholinguistic dictionaries such as the liwc lexicon @xcite , ldi @xcite , and harbingers @xcite . many nlp studies have recently collected lie detection datasets and detected lies using computational models @xcite @xcite ; most of these ignore traditional lie detection methods and findings, and have no follow-up studies, making it difficult to know which datasets are suitable for model training. to use machine learning approaches together with lie detection research in psychology and linguistics, and to seek a way to evaluate and select proper datasets, this study focuses on analyzing verbal leakage cues within; leakage cues hereafter indicate verbal leakage cues. we study leakage cues in terms of the data collection method and model performance. seven lie detection datasets are adopted for experiments. we analyze these datasets using word categories defined in liwc2015 @xcite . through this study, we aim to answer three questions: (1) how do data collection methods affect strong leakage cues? (2) what is the role of the quantity and the category of strong leakage cues in lie detection task? (3) do strong leakage cues contribute to model validity? we expect these answers to help in the construction and selection of appropriate datasets for lie detection tasks. ## leakage cues and datasets to understand how leakage cues contribute to lie detection, we first measure the extent of leakage cues in lie detection datasets. ## leakage cues and model validity to explore the effect of leakage cues on model validity, we conducted both cross-and in-dataset evaluations. we adopted three lie detection models for the experiments: unigru (an, 2014), cnn @xcite , and bert @xcite . ## conclusion in this paper, we study the convolutions among leakage cues, datasets, and models. various conditions are analyzed, with results that show that leakage cues help increase model validity, and that they can be found the most in datasets containing audio statements and lie-based annotations. these findings and the testing methods are good references for selecting appropriate data and models when building lie detection applications. under the condition that no benchmark has been recognized yet, we expect this research to serve as a guide for researchers new to this problem, saving them unnecessary effort and helping them to quickly get up to speed.