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Using text analysis software to detect deception in written short‐answer questions in employee selection
Author(s) -
Forsyth Loch,
Anglim Jeromy
Publication year - 2020
Publication title -
international journal of selection and assessment
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.812
H-Index - 61
eISSN - 1468-2389
pISSN - 0965-075X
DOI - 10.1111/ijsa.12284
Subject(s) - deception , psychology , interpersonal communication , personal pronoun , word (group theory) , social psychology , selection (genetic algorithm) , interpersonal interaction , linguistics , artificial intelligence , computer science , philosophy
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.