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

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;M-age = 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.