Examining embedded lies through computational text analysis.

ABSTRACT: Verbal deception detection research relies on narratives and commonly assumes statements as truthful or deceptive. A more realistic perspective acknowledges that the veracity of statements exists on a continuum, with truthful and deceptive parts being embedded within the same statement. However, research on embedded lies has been lagging behind. We collected a novel dataset of 2,088 truthful and deceptive statements with annotated embedded lies. Using a counterbalanced within-subjects design, participants provided two versions of an autobiographical event. One was described truthfully, and the other one deceptively by including embedded lies. Participants later highlighted those embedded lies and judged them on lie centrality, deceptiveness, and source. We show that a fine-tuned language m

SUBMITTER: Loconte R 

PROVIDER: S-EPMC12280142 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature