# A Corpus-Based Study of Linguistic Deception in Spanish
## 2 Automated Deception Detection
### Essentials of Linguistic Deception Detection  
As stated in [7], context has proved to be an important aspect in research and affects the relation between lying and language. These authors have developed a model called the contextual organization of language and deception (CoLD), which provides a framework including some crucial aspects of context for any deceptive communication. Thus, the nature of the linguistic data in the corpora is worth commenting on. Much has been discussed about the importance of deception in spontaneously produced language. Laboratory-produced lies have been criticized in forensic literature for not being very reliable; for instance, the authors in [8; 9] suggest that further research should involve retrospective studies in law enforcement settings to study realistic responses with known outcomes. However, the strength of laboratory-produced data is the possibility for controlling variables and attributes so that the conclusions drawn are experimentally valid. What remains constant during such an experiment are the participants and the topics on which they write, which allows the researcher to avoid confounding intervening variables and to focus on deception in opinions and memories as the only plausible causal factor. Put another way, providing that some variation is observed regarding the dependent variables analyzed, this scientific control will allow the author to assure that the participants' situations are identical until they are asked to lie, and so the potentially new outcome may be attributed to the independent variable. The usefulness of this kind of corpus has indeed been proved in the forensic context, as shown in such studies as [10]. In this respect, it is also worth noting that there are two types of data: low-stakes deception, in which no harm can be done (it is well known that people lie in social situations without intending harm); and high-stakes deception, where real-life damages are possible and likely. This distinction must be considered when drawing conclusions in automated and computer-assisted deception detection research. Furthermore, a closely related issue in forensic computational linguistics is the importance of working on ground truth data that are forensically feasible. 'Ground truth' data means data for which we know what the correct answers are; thus, for the particular field of deception detection, we need data where we know which texts are true or false. When a method is tested on ground truth data, we can conduct validation testing and accurately report its error rate. In empirical research, validation testing is a technique that determines how well a procedure works, under specific conditions, on a corpus containing texts of known origin [11]. Thus, on a database of ground truth data, the researcher is to apply a replicable analytical method to every text as well as a cross-validation scheme, most typically by building a statistical or a machine learning (ML) model. Last, the error rate is to be computed from the misclassifications in the analysis. Within the research paradigm of forensic computational linguistics, in the present article, a corpus-based study is presented, attempting to answer the question 'Is this truthful or false?' It is worth noting that automated and computer-assisted methods in other corners, such as author identification, are much more consolidated worldwide and generally admitted in court, such as [PERSON]'s SynAID [12; 13], as compared to computer-assisted deception detection, which is not often used for veracity assessment in the legal setting. In other words, in many, if not all, jurisdictions, experts are not allowed to testify that a person is lying, as only the jury or the judge can do it. Thus, deception detection is only an investigative tool, that is to say, its use is restricted to investigation, not trial.