Abstract
In the last decade, fields such as psychology and natural language processing have devoted considerable attention to the automatization of the process of deception detection, developing and employing a wide array of automated and computer-assisted methods for this purpose. Similarly, another emerging research area is focusing on computer-assisted deception detection using linguistics, with promising results. Accordingly, in the present article, the reader is firstly provided with an overall review of the state of the art of corpus-based research exploring linguistic cues to deception as well as an overview on several approaches to the study of deception and on previous research into its linguistic detection. In an effort to promote corpus-based research in this context, this study explores linguistic cues to deception in the Spanish written language with the aid of an automatic text classification tool, by means of an ad hoc corpus containing ground truth data. Interestingly, the key findings reveal that, although there is a set of linguistic cues which contributes to the global statistical classification model, there are some discursive differences across the subcorpora, yielding better classification results on the analysis conducted on the subcorpus containing emotionally loaded language.
Highlights
The distinction between truth and deception has garnered considerable attention from domains such as formal logic and psychological research
Verbal cues to deception have been explored, as the investigation of linguistic cues to deception in written language has proved to be of utmost importance in the forensic context with statements written by witnesses and people involved in crimes, and because in the increase seen by computer-mediated communication, where written texts constitute a fundamental element
This study shows how Text Analysis Toolkit Toward Linguistic Evidence Research (TATTLER) linguistic variables work better than text analysis tools used for different purposes, such as LIWC or simplistic natural language processing (NLP) models, such as bag of words (BoW)
Summary
The distinction between truth and deception has garnered considerable attention from domains such as formal logic and psychological research. In the field of human kinetics, non-verbal communication has been claimed to play a key role in the detection of deception. The field of natural language processing (NLP) has devoted considerable attention to the automatization of the process of deception detection, developing and employing a wide array of automated and computer-assisted methods for this purpose, (see, for example, Ott et al [1] and Quijano-Sanchez et al [2]). Researchers in [3] provide a thorough review of this activity. Another emerging research area is focusing on computer-assisted deception detection using linguistics, [4,5], with promising results. Some computational approaches supervised by experts in the field are considered an efficient way to supplement and support criminal investigators, being of special interest to linguists, jurists, criminologists, and professionals in the field of communications
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