The Effect of Personality Type on Deceptive Communication Style

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.

[1]  Eileen Fitzpatrick,et al.  Building a forensic corpus to test language-based indicators of deception , 2010 .

[2]  K. Locke.  Circumplex Scales of Interpersonal Values: Reliability, Validity, and Applicability to Interpersonal Problems and Personality Disorders , 2000, Journal of personality assessment.

[3]  Jonathan S. Adelstein,et al.  Personality Is Reflected in the Brain's Intrinsic Functional Architecture , 2011, PloS one.

[4]  Walter Daelemans,et al.  Personae: a Corpus for Author and Personality Prediction from Text , 2008, LREC.

[5]  Myers,et al.  Gifts Differing: Understanding Personality Type , 1980 .

[6]  Max Coltheart,et al.  The MRC Psycholinguistic Database , 1981 .

[7]  A. Vrij.  Criteria-Based Content Analysis: A Qualitative Review of the First 37 Studies. , 2005 .

[8]  Fabio Celli.  Unsupervised Personality Recognition for Social Network Sites , 2012, ICDS 2012.

[9]  W. T. Norman,et al.  Toward an adequate taxonomy of personality attributes: replicated factors structure in peer nomination personality ratings. , 1963, Journal of abnormal and social psychology.

[10]  Peter Tiersma,et al.  Author Identification in American Courts , 2004 .

[11]  Ian H. Witten,et al.  Weka: Practical machine learning tools and techniques with Java implementations , 1999 .

[12]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[13]  F. Mosteller,et al.  Inference and Disputed Authorship: The Federalist , 1966 .

[14]  Ron Artstein,et al.  Survey Article: Inter-Coder Agreement for Computational Linguistics , 2008, CL.

[15]  Fabio Celli.  Adaptive Personality Recognition from Text , 2013 .

[16]  Ian Witten,et al.  Data Mining , 2000 .

[17]  P. Costa,et al.  Normal Personality Assessment in Clinical Practice: The NEO Personality Inventory. , 1992 .

[18]  George Forman,et al.  An Extensive Empirical Study of Feature Selection Metrics for Text Classification , 2003, J. Mach. Learn. Res..

[19]  Walter Mischel,et al.  Introduction to personality : toward an integrative science of the person , 2008 .

[20]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[21]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[22]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[23]  J. Pennebaker,et al.  Lying Words: Predicting Deception from Linguistic Styles , 2003, Personality & social psychology bulletin.

[24]  Dong Nguyen,et al.  "How Old Do You Think I Am?" A Study of Language and Age in Twitter , 2013, ICWSM.

[25]  A. Vrij.  Detecting Lies and Deceit: Pitfalls and Opportunities , 2008 .

[26]  Carlo Strapparava,et al.  The Lie Detector: Explorations in the Automatic Recognition of Deceptive Language , 2009, ACL.

[27]  M. Coulthard.  Author Identification, Idiolect, and Linguistic Uniqueness. , 2004 .

[28]  Francisco Iacobelli,et al.  Large Scale Personality Classification of Bloggers , 2011, ACII.

[29]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[30]  Massimo Poesio,et al.  DeCour: a corpus of DEceptive statements in Italian COURts , 2012, LREC.

[31]  Shlomo Argamon,et al.  Effects of Age and Gender on Blogging , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[32]  Elisabeth Dévière,et al.  Analyzing linguistic data: a practical introduction to statistics using R , 2009 .

[33]  Marilyn A. Walker,et al.  Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text , 2007, J. Artif. Intell. Res..

[34]  Jean Carletta,et al.  Assessing Agreement on Classification Tasks: The Kappa Statistic , 1996, CL.

[35]  Eileen Fitzpatrick,et al.  Building a Data Collection for Deception Research , 2012 .

[36]  Walter Daelemans,et al.  Authorship Attribution and Verification with Many Authors and Limited Data , 2008, COLING.

[37]  Julia Hirschberg,et al.  Personality factors in human deception detection: comparing human to machine performance , 2006, INTERSPEECH.

[38]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[39]  Benno Stein,et al.  Proceedings of the SIGIR 2007 International Workshop on Plagiarism Analysis, Authorship Identification, and Near-Duplicate Detection, PAN 2007, Amsterdam, Netherlands, July 27, 2007 , 2007, PAN.

[40]  Massimo Poesio,et al.  Automatic deception detection in Italian court cases , 2013, Artificial Intelligence and Law.

[41]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[42]  Massimo Poesio,et al.  Lexical vs. Surface Features in Deceptive Language Analysis , 2011 .

[43]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[44]  Jon Oberlander,et al.  Whose Thumb Is It Anyway? Classifying Author Personality from Weblog Text , 2006, ACL.