“Tell me who you are” Latent semantic analysis for analyzing spontaneous self-presentations in different situations
Research output: Contribution to journal › Article
The aim of the study was to analyze freely generated self-presentations through the natural language processing technique of Latent Semantic Analysis (LSA). Four hundred fifty-one participants (F = 360; M = 143) recruited from LinkedIn (a professional social network) were randomly assigned to generate 10 words to describe themselves to either an employer (recruitment-condition) or a friend (friendship-condition). The words’ frequency-rate and their semantic representation were compared between condi-tions and to the natural language (Google’s n-gram database). Self-presentations produced in the recruitment condition (vs. natural language) had significantly higher number of agentic words (e.g., problem-solver, responsible, able team-worker) and their contents were semantically closer to the concept of agency (i.e., competence, assertiveness, decisiveness) comparing to the friendship condition. Further-more, the valence of the self-presentations’ words was higher (i.e., with a more positive meaning) in the recruitment condition. Altogether, these findings are consistent with the literature on the “Big Two,” self-presentation, and impression management.
|Research areas and keywords||
Subject classification (UKÄ) – MANDATORY
|Number of pages||18|
|Journal||TPM - Testing, Psychometrics, Methodology in Applied Psychology|
|Publication status||Published - 2020 Jun|