Detecting deceptive communication through linguistic concreteness

Abstract

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.

La produzione di testi semplificati di notiziari televisivi italiani destinati a persone con disturbi cognitivi acquisiti: un’integrazione tra metodi psicolinguistici e analisi automatiche

“…Further, deceptive texts seem to be related with an increased use of adverbs in both datasets. This can be related to the less concreteness of deceptive texts as discussed in Kleinberg et al (2019) and it is in line with many theories of deception like the Reality Monitoring (Johnson et al, 1998), Criteria based Content Analysis (Undeutsch, 1989) and Verifiability Approach (Nahari et al, 2014). This also explains the prevalence of the number of named entities, spatial related words, conjunctions and WDAL imagery score in truthful texts in the enAFD dataset and the use of more motion verbs in deceptive texts in the elAFD dataset.…”

Linguistic concreteness of statements of true and false intentions.

“…To our knowledge, the only other study to have examined differences in concreteness between true and false statements of intent is Kleinberg et al (2019). They used eight measures of concreteness on true and false written statements from six data sets, three of which examined true and false intentions.…”

The Grievance Dictionary: Understanding threatening language use

“…We compute Cronbach's alpha using the proportional occurrence of each word in the 22 categories for a total of 17,583 texts across four corpora (Table 2). Similar to the development of LIWC2015, we use a varied selection of texts to compute reliability, including texts from deception detection experiments (Kleinberg, van der Vegt, Arntz, & Verschuere, 2019), novels (Lahiri, 2014), movie reviews (Maas et al, 2011), and Reddit posts (Demszky et al, 2020).…”

“…Note. a Hotel reviews (Ott, Choi, Cardie, & Hancock, 2011;Ott, Cardie, & Hancock, 2013), descriptions of past and planned activities (Kleinberg et al, 2019) example, several Grievance Dictionary categories such as frustration, grievance, hate, murder, paranoia, surveillance, violence, and weaponry were positively correlated to the LIWC category negative emotion. Hate, murder, surveillance, threat, and violence were also positively related to the LIWC's anger category.…”

“…(Lahiri, 2014) 3,036 (247,142,420) IMDB movie reviews (Maas et al, 2011) 50,000 (13,934,687) Reddit posts (Demszky et al, 2020) 70,000 (1,081,539) Note. *Hotel reviews (Ott et al, 2011(Ott et al, , 2013, descriptions of past and planned activities (Kleinberg et al, 2019) A psychometric evaluation was performed for each version of the dictionary (words with a rating of 7 or higher, words with a rating of 5 or higher, weighted words). The results reported from here onwards concern the dictionary using words with a rating of 7 or higher, because this dictionary performed best (results for the other versions are available on the OSF).…”