DocumentCode :
2125857
Title :
The Effect of Personality Type on Deceptive Communication Style
Author :
Fornaciari, Tommaso ; Celli, Fabio ; Poesio, Massimo
Author_Institution :
CIMeC, Univ. of Trento, Rovereto, Italy
fYear :
2013
fDate :
12-14 Aug. 2013
Firstpage :
1
Lastpage :
6
Abstract :
It has long been hypothesized that the ability to deceive depends on personality - some personality types are `better´ at deceiving in that their deception is harder to recognize. In this work, we evaluate how the pattern of personality of a speaker affects the effectiveness of machine learning models for deception detection in transcripts of oral speech. We trained models to classify as deceptive or not deceptive statements issued in Court by Italian speakers. We then used a system for automatic personality recognition to generate hypotheses about the personality of these speakers, and we clustered the subjects on the basis of their personality traits. It turned out that deception detection models perform differently depending on the patterns of personality traits which characterize the speakers. This suggests that speakers who show certain types of personality also have a communication style in which deception can be detected more, or less, easily.
Keywords :
behavioural sciences computing; learning (artificial intelligence); pattern classification; psychology; speech recognition; text analysis; Court; Italian speakers; automatic personality recognition; deception detection model; deceptive communication style; deceptive statement; machine learning models; not deceptive statement; oral speech; personality trait pattern; personality types; speaker personality pattern; Accuracy; Artificial neural networks; Auditory system; Correlation; Decision trees; Feature extraction; Text recognition; deception detection; natural language processing; personality recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligence and Security Informatics Conference (EISIC), 2013 European
Conference_Location :
Uppsala
Type :
conf
DOI :
10.1109/EISIC.2013.8
Filename :
6657118
Link To Document :
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