Detecting Deception Using MCI in Twitter

Authors: Morgan CA*, Barthen J and Mol SA

DOI: 10.23880/ijfsc-16000358

Volume 9, Issue 1

Abstract

The purpose of the study was to test whether Modified Cognitive Interviewing (MCI) is an effective method for detecting deceptive human eyewitness accounts in a computer-mediated computer platform with limited text space (such as in Twitter). 44 college students were the participants in this study, where they either had to perform, or pretend that they had performed a cognitive task. Of the 44 participants, 15 performed the task and reported truthfully about their activities; 16 performed the task and denied having participated in the task; 13 read the instructions about the cognitive task and when interviewed claimed to have actually performed the task. The transcripts of interviews, conducted in Twitter, were rated by individuals trained in cognitive interviewing; forensic speech variables (response length (RL), unique word (UW) count and type-token ratio (TTR) were coded from transcripts. Human rater judgments and computer-based speech analysis performed better than chance; computer based judgments were superior to the human judgments (i.e., 79% vs. 54%, respectively). Speech content variables derived from MCI differed significantly, and in different ways, between the truthful and false claimant participants and also between the truthful and denial type participants. MCI derived statement analysis methods are a scientifically valid method, when used in Twitter, that can be used by professionals tasked with distinguishing between true claims, false claims and denials.

Keywords: Social Media; Eyewitness Memory; Lying; “X”; Statement Analysis

View PDF