Date of Award

Spring 6-1-2019

Document Type

Thesis

Degree Name

Master of Arts (MA)

Department/Program

Forensic Psychology

Language

English

First Advisor

Saul Kassin

Second Reader

Tammy Gales

Third Advisor

Deryn Strange

Abstract

Confessions are considered the gold standard of evidence, and yet many cases of false confessions causing wrongful convictions have come to the surface in the past decades. Currently, a method to identify false confessions does not exist and studies focusing on the content of the confessions have found similarities rather than points of distinction. In this study, we approached confessions from a stylistic rather than qualitative point of view, utilizing corpus analysis to outline the linguistic features of two samples of confessions: false confessions (n=37) and confessions not in dispute (n=98). Subsequently, we created a model through logistic regression in order to distinguish the two, based on three predictors: personal pronouns, impersonal pronouns and conjunctions. In a first sample comprised of 25 false confessions and 25 confessions not in dispute the model correctly categorized 37 out of 50 confessions (74% accuracy rate), and in a second out-of-model sample the predictors accurately classified 20 of the 24 confessions (83.3% accuracy rate). A high frequency of impersonal pronouns was found to be associated with a higher likelihood of the confession being false, while confessions containing a higher rate of conjunctions and personal pronouns were more likely to belong to the sample not in dispute. Lastly, in confessions not in dispute “I” was often followed by a lexical verb, a pattern that was found with less frequency in false confessions. Disparities were also found in the way variations of the sentence “I don’t remember” were used within the corpora.

Available for download on Tuesday, December 24, 2019

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