The Kernel of Truth in Text-Based Personality Assessment: A Meta-Analysis of the Relations Between the Big Five and the Linguistic Inquiry and Word Count (LIWC)

Abstract

The Linguistic Inquiry and Word Count (LIWC) is a popular closed-vocabulary text analysis software program that is used to understand whether individuals’ use of linguistic categories (i.e., word categories, such as negative affect) depends on their personality traits. Here, we present the first meta-analysis of the relations between the Big Five personality traits and 52 linguistic categories of the English language. Across 31 eligible samples (n = 85,724), the results showed that (a) self-reported personality traits are significantly correlated with linguistic categories, but the effect sizes are relatively small (the strongest effect sizes between the Big Five and linguistic categories ranged from |ρ| =.08 to.14, and the 52 LIWC categories explained on average 5.1% of personality variance); (b) observerreported personality traits are significantly correlated with linguistic categories, with the effect sizes being small-to-medium (|ρ| =.18–.39, explaining 38.5% of personality variance); (c) 20 linguistic categories (out of 260; 5 Personality Traits × 52 LIWC Categories) correlated both with self and observer-reported personality traits (the “kernel of truth” in linguistic markers of personality); and (d) 10 study, sample, and task characteristics significantly moderated the correlations of the linguistic categories with personality traits, showing that the effect sizes were mainly stronger for longer texts and older LIWC versions, among others.