# A Corpus-Based Study of Linguistic Deception in Spanish
## 3 Description and Explanation of the Most Significant Methodologies
### Software Developed for the Computational Classification of Written Statements as True or False  
The software specifically developed for linguistic deception detection is presented in this section. One of the most famous methods for deception detection is scientific content analysis (SCAN). It was developed in 1987 [37], a polygraph examiner, and methods based on it are generally known as statement analysis. Most of the literature published on this type of analysis is merely descriptive (see, for example, [PERSON] [38] and [PERSON] [39]), although it was automated with reported accuracy results of 71% in [10]. However, as stated in [40], SCAN and other statement analysis systems have been mainly used and taught by practitioners manually, with several studies having examined SCAN with suggestive but inconsistent results [41; 42]. Some other computational tools have been specifically developed for deception detection, such as Agent99 Analyzer [43], created to extract linguistic cues to deception from texts and videos, iSKim [44], or CueCal [14]. A somewhat different detection deception software is ADAM, or automated deception analysis machine [45], which focuses on editing processes, such as backspace or spacebar while typing messages as well as measuring response latencies. The main methodological drawback of this approach seems to be that it requires a keystroke analyzer to be on the interviewee's machine, which can be seen as an intrusion of privacy. Remarkably, most previous studies in computerized deception detection have relied exclusively on shallow lexico-syntactic patterns. However, [19] were the first researchers to explore syntactic stylometry. Over four different subcorpora including service reviews and essays on different topics, the authors explore features derived from phrase structure grammar (PSG) parse trees, showing that they consistently improve the detection rate over several baselines that are based only on lexical features. Most relevantly, within the four subcorpora examined, they apply their method to the corpus from TripAdvisor collected for [1], improving the classification results obtained by its collectors by reaching over 91% accuracy. In this line of linguistic sophistication, a valuable contribution to linguistic deception detection has been made by Witness Statement Evaluation Research (WISER), one of the tools provided by ALIAS Technology ([[https://aliastechnology.com/](https://aliastechnology.com/)]([https://aliastechnology.com/](https://aliastechnology.com/)), accessed on 4 July 2021), a company which offers forensic linguistics consulting to attorneys, law enforcement, human resources, and security teams. WISER is a project that makes use of automated text analysis and statistical classifiers to determine the best protocol for the computational classification of true and false statements in the forensic-investigative setting. Ref. [4] tested this text analysis tool, based on ALIAS's module Text Analysis Toolkit Toward Linguistic Evidence Research (TATTLER). It combines linguistic analysis at the phonological, syntactic, and lexico-semantic levels and has been applied to deception detection classification on two types of corpora: low-stakes (laboratory) and high-stakes, actual statements in criminal investigations [46]. The low-stakes, laboratory data comprised two narratives of a traumatic experience, one truthful and the other false, from each participant, while the high-stakes data consisted of actual statements from real criminal investigations with non-linguistic evidence of their veracity or falsehood. The WISER method yielded substantially different results, as 71% of the texts in the laboratory corpus were correctly identified, using leave-one-out cross-validation, while the rate reached 93% for high-stakes deception, which can be considered the most successful rate published to date. Furthermore, this brings to light the contrast between lies told in a low-stakes, laboratory setting and those told in a police investigation.