Skip to main content Accessibility help
Hostname: page-component-8bbf57454-nshs2 Total loading time: 0.494 Render date: 2022-01-21T23:07:58.343Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": true, "newCiteModal": false, "newCitedByModal": true, "newEcommerce": true, "newUsageEvents": true }

13 - Computational Approaches for Personality Prediction

from Part II - Machine Analysis of Social Signals

Published online by Cambridge University Press:  13 July 2017

Bruno Lepri
Bruno Kessler Foundation
Fabio Pianesi
Bruno Kessler Foundation
Judee K. Burgoon
University of Arizona
Nadia Magnenat-Thalmann
Université de Genève
Maja Pantic
Imperial College London
Alessandro Vinciarelli
University of Glasgow
Get access



In everyday life, people usually describe others as being more or less talkative or sociable, more or less angry or vulnerable to stress, more or less planful or behaviorally controlled. Moreover, people exploit these descriptors in their everyday life to explain and/or predict others’ behavior, attaching them to well-known as well as to new acquaintances. In all generality, the attribution of stable personality characteristics to others and their usage to predict and explain their behavior is a fundamental characteristics of human naive psychology (Andrews, 2008).

As agents that in increasingly many and varied ways participate in and affect the lives of humans, computers need to explain and predict their human parties’ behavior by, for example, deploying some kind of naive folk-psychology in which the understanding of people's personality can reasonably be expected to play a role. In this chapter, we address some of the issues that attempts at endowing machines with the capability of predicting people's personality traits.

Scientific psychology has developed a view of personality as a higher-level abstraction encompassing traits, sets of stable dispositions toward action, belief, and attitude formation. Personality traits differ across individuals, are relatively stable over time, and influence behavior. Between-individual differences in behavior, belief, and attitude can therefore be captured in terms of the dispositions/personality traits that are specific to each individual, in this way providing a powerful descriptive and predictive tool that has been widely exploited by, for example, clinical and social psychology, educational psychology, and organizational studies.

The search for personality traits has been often pursued by means of factor-analytic studies applied to lists of trait adjectives, an approach based on the lexical hypothesis (Allport & Odbert, 1936), which maintains that the most relevant individual differences are encoded into the language, and the more important the difference, the more likely it is to be expressed as a single word.

Social Signal Processing , pp. 168 - 182
Publisher: Cambridge University Press
Print publication year: 2017

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)


Allport, G.W. & Odbert, H. S. (1936). Trait-names: A psycho-lexical study. Psychological Monographs, 47, 1–171.Google Scholar
Ambady, N., Bernieri, F. J., & Richeson, J. A. (2000). Toward a histology of social behavior: Judgmental accuracy from thin slices of the behavioral stream. In M. P., Zanna (Ed.), Advances in Experimental Social Psychology (vol. 32, pp. 201–271). San Diego: Academic Press.
Ambady, N. & Rosenthal, R. (1992). Thin slices of expressive behavior as predictors of interpersonal consequences: A meta-analysis. Psychological Bulletin, 111(2), 256–274.Google Scholar
André, E., Klense, M., Gebhard, P., Allen, S., & Rist, T. (1999). Integrating models of personality and emotions into lifelike characters. In Proceedings of the Workshop on Affect in Interaction – Towards a New Generation of Interfaces (pp. 136–149).
Andrews, K. (2008). It's in your nature: A pluralistic folk psychology. Synthese, 165(1), 13–29.Google Scholar
Batrinca, L. M., Lepri, B., Mana, N., & Pianesi, F. (2012). Multimodal recognition of personality traits in human–computer collaborative tasks. In Proceedings of the 14th International Conference on Multimodal Interaction (ICMI'12).
Batrinca, L.M., Mana, N., Lepri, B., Pianesi, F., & Sebe, N. (2011). Please, tell me about yourself: Automatic personality assessment using short self-presentations. In Proceedings of the 13th International Conference on Multimodal Interfaces (ICMI '11), pp. 255–262.
Bell, S. T. (2007). Deep-level composition variables as predictors of team performance: A metaanalysis. Journal of Applied Psychology, 92(3), 595–615.Google Scholar
Berlingerio, M., Coscia, M., Giannotti, F., Monreale, A., & Pedreschi, D. (2013). Multidimensional networks: Foundations of structural analysis. World Wide Web, 16, 567.Google Scholar
Borgatti, S. P. & Foster, P. (2003). The network paradigm in organizational research: A review and typology. Journal of Management, 29(6), 991–1013.Google Scholar
Borkenau, P. & Liebler, A. (1992). Traits inferences: Sources of validity at zero acquaintance. Journal of Personality and Social Psychology, 62, 645–657.Google Scholar
Borkenau, P. & Liebler, A. (1993). Convergence of stranger ratings of personality and intelligence with self-ratings, partner ratings and measured intelligence. Journal of Personality and Social Psychology, 65, 546–553.Google Scholar
Breiman, L. (2001). Random forest. Machine Learning, 45(1), 5–32.Google Scholar
Cattell, R. B. (1957). Personality and Motivation: Structure and Measurement. New York: Harcourt, Brace & World.
Chittaranjan, G., Blom, J., & Gatica-Perez, D. (2011). Who's who with Big-Five: Analyzing and classifying personality traits with smartphones. In Proceedings of International Symposium on Wearable Computing (ISWC 2011).
Chittaranjan, G., Blom, J., & Gatica-Perez, D. (2013). Mining large-scale smartphone data for personality studies. Personal and Ubiquitous Computing, 17(3), 433–450.Google Scholar
Costa, P. T. & McCrae, R. R. (1992). Four ways why five factors are basic. Personality and Individual Differences, 13, 653–665.Google Scholar
Dabbs, J. M. & Bernieri, F. J. (1999). Judging personality from thin slices. Unpublished data. University of Toledo.
De Montjoye, Y. A., Quoidbach, J., Robic, F., & Pentland, A. (2013). Predicting personality using novel mobile phone-based metrics. In Proceedings of Social BP (pp. 48–55).
DeNeve, K. M. & Cooper, H. (1998). The happy personality: A meta-analysis of 137 personality traits and subjective well-being. Psychological Bulletin, 124(2), 197–229.Google Scholar
Fleeson, W. (2001). Toward a structure- and process-integrated view of personality: Traits as density distributions of states. Journal of Personality and Social Psychology, 80, 1011–1027.Google Scholar
Friggeri, A., Lambiotte, R., Kosinski, M., & Fleury, E. (2012). Psychological aspects of social communities. In Proceedings of IEEE Social Computing (SocialCom 2012).
Funder, D. C. & Sneed, C. D. (1993). Behavioral manifestations of personality: An ecological approach to judgmental accuracy. Journal of Personality and Social Psychology, 64, 479–490.Google Scholar
Furnham, A. & Fudge, C. (2008). The Five Factor model of personality and sales performance. Journal of Individual Differences, 29(1), 11–16.Google Scholar
Goren-Bar, D., Graziola, I., Pianesi, F., & Zancanaro, M. (2006). Influence of personality factors on visitors' attitudes towards adaptivity dimensions for mobile museum guides. User Modeling and User Adapted Interaction: The Journal of Personalization Research, 16(1), 31–62.Google Scholar
Grucza, R. A. & Goldberg, L. R. (2007). The comparative validity of 11 modern personality inventories: Predictions of behavioral acts, informant reports, and clinical indicators. Journal of Personality Assessment, 89, 167–187.Google Scholar
Hogan, R., Curphy, G. J., & Hogan, J. (1994). What we know about leadership: Effectiveness and personality. American Psychologist, 49(6), 493–504.Google Scholar
Hurtz, G. M. & Donovan, J. J. (2000). Personality and job performance: The Big Five revisited. Journal of Applied Psychology, 85, 869–879.Google Scholar
Janis, L. (1954). Personality correlates of susceptibility to persuasion. Journal of Personality, 22(4), 504–518.Google Scholar
John, O. P. & Srivastava, S. (1999). The Big Five trait taxonomy: History, measurement, and theoretical perspectives. In L. A., Pervin & O. P., John (Eds), Handbook of Personality: Theory and Research (pp. 102–138). New York: Guilford Press.
Judge, T. A., Bono, J. E., Ilies, R., & Gerhardt, M. W. (2002). Personality and leadership: A qualitative and quantitative review. Journal of Applied Psychology, 87(4), 765–780.Google Scholar
Judge, T. A., Heller, D., & Mount, M. K. (2002). Five-factor model of personality and job satisfaction: A meta-analysis. Journal of Applied Psychology, 87(3), 530–541.Google Scholar
Kalimeri, K., Lepri, B., & Pianesi, A. (2013). Going beyond traits: Multimodal recognition of personality states in the wild. In Proceedings of 15th International Conference on Multimodal Interfaces (pp. 27–34).
Kalish, Y. & Robins, G. (2006). Psychological predisposition and network structure: The relationship between individual predispositions, structural holes and network closure. Social Networks, 28, 56–84.Google Scholar
Klein, K. J., Lim, B. C., Saltz, J. L., & Mayer, D. M. (2004). How do they get there? An examination of the antecedents of network centrality in team networks. Academy of Management Journal, 47, 952–963.Google Scholar
Lepri, B., Staiano, J., Rigato, G., et al. (2012). The SocioMetric Badges Corpus: A multilevel behavioral dataset for social behavior in complex organizations. In Proceedings of IEEE Social Computing (SocialCom 2012).
Lepri, B., Subramanian, R., Kalimeri, K., et al. (2010). Employing social gaze and speaking activity for automatic determination of the Extraversion trait. In Proceedings of International Conference on Multimodal Interaction (ICMI 2010).
Lepri, B., Subramanian, R., Kalimeri, K., et al. (2012). Connecting meeting behavior with Extraversion – A systematic study. IEEE Transactions on Affective Computing, 3(4), 443–455.Google Scholar
Mairesse, F., Walker, W. A., Mehl, M. R., & Moore, R. K. (2007). Using linguistic cues for the automatic recognition of personality in conversation and text. Journal of Artificial Intelligence Research, 30, 457–500.Google Scholar
Mohammadi, G. & Vinciarelli, A. (2012). Automatic personality perception: Prediction of trait attribution based on prosodic features. IEEE Transactions on Affective Computing, 3(3), 273– 284.Google Scholar
Murray, H. G., Rushton, J. P., & Paunonen, S. V. (1990). Teacher personality traits and student instructional ratings in six types of university courses. Journal of Educational Psychology, 82(2), 250–261.Google Scholar
Olguin Olguin, D.,Waber, B., Kim, T., et al. (2009). Sensible organizations: Technology for automatically measuring organizational behavior. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 39(1), 43–55 Google Scholar
Peabody, D. & Goldberg, L. R. (1989). Some determinants of factor structures from personalitytrait descriptors. Journal of Personality and Social Psychology, 57, 552–567.Google Scholar
Pianesi, F., Mana, N., Cappelletti, A., Lepri, B., & Zancanaro, M. (2008). Multimodal recognition of personality traits in social interactions. In Proceedings of ACM-ICMI '08.
Pollet, T. V., Roberts, S. G. B., & Dunbar, R. I. M. (2011). Extraverts have larger social network layers but do not feel emotionally closer to individuals at any layer. Journal of Individual Differences, 32(3), 161–169.Google Scholar
Roberts, S. G. B., Wilson, R., Fedurek, P., & Dunbar, R. I. M. (2008). Individual differences and personal social network size and structure. Personality and Individual Differences, 4, 954–964.Google Scholar
Scherer, K. R. (1978). Inference rules in personality attribution from voice quality: The loud voice of extraversion. European Journal of Social Psychology, 8, 467–487.Google Scholar
Staiano, J., Lepri, B., Aharony, N., et al. (2012). Friends don't lie – inferring personality traits from social network structure. Proceedings of UbiComp 2012.
Tapus, A., Tapus, C., & Mataric, M. (2008). User-robot personality matching and robot behavior adaptation for post-stroke rehabilitation therapy. Intelligent Service Robotics Journal(special issue on multidisciplinary collaboration for socially assistive robotics), 1(2), 169–183.Google Scholar
Zen, G., Lepri, B., Ricci, E., & Lanz, O. (2010). Space speaks: Towards socially and personality aware visual surveillance. In Proceedings ofWorkshop on Multimodal Pervasive Video Analysis (MPVA 2010), in conjunction with ACM Multimedia.
Zhou, X. & Conati, C. (2003). Inferring user goals from personality and behavior in a causal model of user affect. In Proceedings of the 8th International Conference on Intelligent User Interfaces (IUI'03).
Asendorpf, J. B. & Wilpers, S. (1998). Personality effects on social relationships. Journal of Personality and Social Psychology, 74, 1531–1544.Google Scholar
Cassell, J. & Bickmore, T. (2003). Negotiated collusion: Modeling social language and its relationship effects in intelligent agents. User Modeling and User-Adapted Interaction, 13, 89–132.Google Scholar
Donnellan, M. B., Conger, R. D., & Bryant, C. M. (2004). The Big Five and enduring marriages. Journal of Research in Personality, 38, 481–504.Google Scholar
Komarraju, M. & Karau, S. J. (2005). The relationship between the Big Five personality traits and academic motivation. Personality and Individual Differences, 39, 557–567.Google Scholar
Reeves, B. & Nass, C. (1996). The Media Equation. Chicago: University of Chicago Press.
Rotter, J. B. (1965). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs, 80(1), 1–28.Google Scholar
Sigurdsson, J. F. (1991). Computer experience, attitudes toward computers and personality characteristics in psychology undergraduates. Personality and Individual Differences, 12(6), 617– 624.Google Scholar
Snyder, M. (1974). Self-monitoring of expressive behavior. Journal of Personality and Social Psychology, 30, 526–537.Google Scholar
Cited by

Send book to Kindle

To send this book to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle.

Note you can select to send to either the or variations. ‘’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats

Send book to Dropbox

To send content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to Dropbox.

Available formats

Send book to Google Drive

To send content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to Google Drive.

Available formats