Cross-cultural Deception Detection

Verschuere²,

Vegt³

2019

Preprint

Several research lines attempted to tell truthful from deceptive texts by looking at the concreteness in language as an indicator of truthfulness. We identified eight different operationalizations of concreteness for computerautomated analysis and validated these operationalizations on six diverse datasets containing truthful and deceptive texts (about hotel reviews, past and future weekend plans, as well as intended flight plans). The results suggest that not just the efficacy but also the directionality of concreteness as a cue to deception is dependent on the operationalization and the context. A predictive analysis suggests that concreteness indices can differentiate between truthful and deceptive statements above the chance level and are a worthy path for future research. The importance of conducting multi-dataset and multi-operationalization investigations in linguistic deception detection research is highlighted.

Section: Are Truth Tellers or Liars More Concrete?supporting

confidence: 79%

Detecting deceptive communication through linguistic concreteness

Verschuere²,

Vegt³

2019

Preprint

“…The accuracy rates will per definition be higher if the classifier is trained and tested on the same data, compared with a proper validation on a new sample (Yarkoni & Westfall, ). Although data from the first experiment suggest accuracies of up to 80%, the independent‐sample validation indicated that the true boundaries might be closer to 63% (similar accuracies using automated analysis were achieved by Pérez‐Rosas & Mihalcea, ). We strongly recommend that future research that makes claims about prediction incorporate a cross‐validation (e.g., train–test split or leave‐one‐out cross‐validation) and proper, actual validation on a new sample to avoid the reporting of overestimated accuracies.…”

Section: Discussionmentioning

confidence: 52%

“…Each word category is composed of a comprehensive dictionary, and the analytical task consists of counting the number of words per category. Several studies have successfully used the LIWC to discriminate lies from truths (Bond & Lee, ; Kleinberg, Mozes, Arntz, & Verschuere, ; Mihalcea & Strapparava, ; Ott, Choi, Cardie, & Hancock, ; Pérez‐Rosas & Mihalcea, ).…”

Section: Introductionmentioning

confidence: 99%

Automated verbal credibility assessment of intentions: The model statement technique and predictive modeling

Applied Cognitive Psychology

Toolen

Vrij

et al. 2018

SummaryRecently, verbal credibility assessment has been extended to the detection of deceptive intentions, the use of a model statement, and predictive modeling. The current investigation combines these 3 elements to detect deceptive intentions on a large scale. Participants read a model statement and wrote a truthful or deceptive statement about their planned weekend activities (Experiment 1). With the use of linguistic features for machine learning, more than 80% of the participants were classified correctly. Exploratory analyses suggested that liars included more person and location references than truth‐tellers. Experiment 2 examined whether these findings replicated on independent‐sample data. The classification accuracies remained well above chance level but dropped to 63%. Experiment 2 corroborated the finding that liars' statements are richer in location and person references than truth‐tellers' statements. Together, these findings suggest that liars may over‐prepare their statements. Predictive modeling shows promise as an automated veracity assessment approach but needs validation on independent data.

“…Underlying each category are extensive dictionaries of words against which the words in the statements are analyzed. LIWC has successfully been employed in multiple contexts (Ott, Cardie, & Hancock, 2013;Pérez-Rosas & Mihalcea, 2014) and was shown to be acceptable for modeling human-coded RM annotation (Bond & Lee, 2005). In the current investigation, we used the LIWC word categories "percept," "space" and "time," to model perceptual, spatial and temporal details, respectively.…”

mentioning

confidence: 99%

An Investigation on the Detectability of Deceptive Intent about Flying through Verbal Deception Detection

Nahari²,

Arntz

et al. 2017

Collabra: Psychology

Background: Academic research on deception detection has largely focused on the detection of past events. For many applied purposes, however, the detection of false reports about someone's intention merits attention. Based on the verbal deception detection paradigm, we explored whether true statements on intentions were more detailed and more specific than false statements on intentions, particularly when instructed to be as specific as possible. Method: Participants (n = 222) lied or told the truth about their upcoming travel plans either providing 'as much information as possible' (standard instructions) or being 'as specific as possible' (i.e., mentioning times, locations, places; specific instructions), resulting in four conditions (truthful vs. deceptive intention by standard vs. specific instructions). We collected data via a custom-made web app and performed automated verbal content analysis of participants' written answers. Findings: We did not find a significant difference in the specificity of participants' statements. The instruction to be as specific as possible promoted more specific information but did not help to discern honest from deceptive flying intentions. Conclusion:The experiment reported here attempted to demonstrate automated verbal deception detection of intentions. The difficulty in capturing genuine intentions, and the non-intrusive, noninteractive questioning approach might explain the null findings and raise questions for further research. We conclude with suggestions for a novel framework on semi-interactive information elicitation. With an increased demand for security systems like airport border control, researchers and practitioners alike have identified the need for applications to detect deception on a large scale (Honts & Hartwig, 2014;Vrij, Granhag, & Porter, 2010). For example, the context of airport border control excludes many tools used in deception research due to their limited applicability. With developments towards more seamless passenger flows and minimal passenger-security personnel interaction, an ideal deception detection system would be implementable at stages even before passengers arrive at the airport (e.g., a filter system during online check-in processes, Kleinberg, Arntz, & Verschuere, in press). A promising paradigm that might be applicable in settings that require the testing of a vast number of people is verbal deception detection. However, because the majority of verbal deception detection studies were conducted on the verbal content of face-to-face interviews, a key challenge is the transition towards large-scale applicable methods. This paper reports an attempt to apply verbal deception detection tools on a large-scale in an airport security context. Verbal deception detectionThe idea to use the verbal content as an indicator of deception is rooted in the Undeutsch Hypothesis (1967, 1982) stating that truthful statements differ from false declarations in quality and content because the process through which the particular statement is produced is d...