Using text analysis software to detect deception in written short‐answer questions in employee selection

Abstract

This study investigated if word frequencies informed by the Newman‐Pennebaker (NP) and Reality Monitoring (RM) models could classify honest and deceptive responses to short‐answer questions often used in online employee applications. Participants (n = 106; 58% male; Mage = 30.28 years, SD = 8.85) completed two written short‐answer questions both deceptively and honestly. The questions asked participants to describe a notable personal achievement or a time where they had demonstrated interpersonal skills. Linguistic Inquiry and Word Count was used to calculate the prevalence of words in various linguistic categories. Deceptive statements contained significantly fewer first‐person singular pronouns, auxiliary verbs, adverbs, conjunctions, and cognitive process words. Results revealed the NP and RM models accuracy at classifying responses varied on question type.

Computational Measures of Deceptive Language: Prospects and Issues

“…Second, computational stylometry has been tested for its ability to detect deception with various tools and methods (e.g., for LIWC, see Ali and Levine, 2008;Fornaciari and Poesio, 2013;Newman et al, 2003;Tomas et al, 2021c; for named entity recognition, see Kleinberg et al, 2018; for morpho-syntactic labeling, see Banerjee and Chua, 2014; for n-grams, see Cagnina and Rosso, 2017;Hernández Fulsilier et al, 2015;Ott et al, 2013; for vector representations, see Nam et al, 2020; for BERT, see Barsever et al, 2020). Third, it has been the subject of over 20 peer-reviewed publications (e.g., Hauch et al, 2015;Forsyth and Anglim, 2020;Tomas et al, 2021a).…”

Is a cover letter still needed? Using LIWC to predict application success

“…In psychological studies LIWC was used to investigate the connection between language and individual differences, social processes or mental health. For example, LIWC was applied to identify gender differences (Gaucher, Friesen, & Kay, 2011; Kanze, Huang, Conley, & Higgins, 2018), hierarchical processes (Kacewicz, Pennebaker, Davis, Jeon, & Graesser, 2014; Markowitz, 2018), deception (Forsyth & Anglim, 2020; Hancock, Curry, Goorha, & Woodworth, 2007) or authorship (Boyd & Pennebaker, 2015). LIWC has been translated into numerous languages and is one of the most widely used text analysis software (for review see Chung & Pennebaker, 2018; Pennebaker, 2013; Tausczik & Pennebaker, 2010).…”