PersonalitySensing: A Multi-View Multi-Task Learning Approach for Personality Detection based on Smartphone Usage

Assessing individual's personality traits has important implications in psychology, sociology, and economics. Conventional personality measurement methods were questionnaire-based, which are time-consuming and manpower-expensive. With the pervasive deployment of mobile communication applications, smartphone usage data was found to relate to people's social behavioral and psychological aspects. In this paper, we propose a deep learning approach to infer people's Big Five personality traits based on smartphone data. Specifically, we collect smartphone usage snapshots with an Android App, and extract features from the collected data. We propose a multi-view multi-task learning approach with a deep neural network model to fuse the extracted features and learn the Big Five personality traits jointly. Extensive experiments based on the real-world smartphone data collected from university volunteers show that the proposed approach significantly outperforms the state-of-the-art algorithms in personality prediction.

[1]  Xiaoning Qian,et al.  Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data , 2018, NeurIPS.

[2]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[3]  Nicu Sebe,et al.  Friends don't lie: inferring personality traits from social network structure , 2012, UbiComp.

[4]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[5]  Joachim Mathiesen,et al.  Phone-based Metric as a Predictor for Basic Personality Traits , 2016, Journal of Research in Personality.

[6]  Rich Caruana,et al.  Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.

[7]  I. Deary,et al.  Personality traits, 2nd ed. , 2003 .

[8]  Geoffrey E. Hinton,et al.  Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer , 2017, ICLR.

[9]  James G. Phillips,et al.  Personality and self reported mobile phone use , 2008, Comput. Hum. Behav..

[10]  Daniel Gatica-Perez,et al.  Mining large-scale smartphone data for personality studies , 2013, Personal and Ubiquitous Computing.

[11]  T Kron,et al.  How to become a better leader. , 1976, Nursing.

[12]  T. Ownsworth,et al.  The Big Five personality factors and psychological well-being following stroke: a systematic review , 2019, Disability and rehabilitation.

[13]  Kyriaki Kalimeri,et al.  Going beyond traits: multimodal classification of personality states in the wild , 2013, ICMI '13.

[14]  L. R. Goldberg.  The structure of phenotypic personality traits. , 1993, The American psychologist.

[15]  Howard S. Friedman,et al.  Personality: Classic Theories and Modern Research , 1998 .

[16]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.

[17]  Ya Zhang,et al.  Multi-task learning for boosting with application to web search ranking , 2010, KDD.

[18]  P. J. Huber.  Robust Estimation of a Location Parameter , 1964 .

[19]  Alex Pentland,et al.  Predicting Personality Using Novel Mobile Phone-Based Metrics , 2013, SBP.

[20]  Nuria Oliver,et al.  Towards a psychographic user model from mobile phone usage , 2011, CHI Extended Abstracts.

[21]  Aboul Ella Hassanien,et al.  Predicting Personality Traits and Social Context Based on Mining the Smartphones SMS Data , 2015, ECC.

[22]  Daniel Gatica-Perez,et al.  Who's Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones , 2011, 2011 15th Annual International Symposium on Wearable Computers.

[23]  P. Costa,et al.  Revised NEO Personality Inventory (NEO-PI-R) and NEO-Five-Factor Inventory (NEO-FFI) , 1992 .

[24]  Jason Weston,et al.  A Neural Attention Model for Abstractive Sentence Summarization , 2015, EMNLP.

[25]  Martial Hebert,et al.  Cross-Stitch Networks for Multi-task Learning , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Frederick R. Forst,et al.  On robust estimation of the location parameter , 1980 .

[27]  D. Lynam,et al.  Using the five-factor model to represent the DSM-IV personality disorders: an expert consensus approach. , 2001, Journal of abnormal psychology.

[28]  Massimiliano Pontil,et al.  Exploiting Unrelated Tasks in Multi-Task Learning , 2012, AISTATS.

[29]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[30]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[31]  Cecilia Mascolo,et al.  Putting mood in context: Using smartphones to examine how people feel in different locations , 2017 .

[32]  Gabriella M. Harari,et al.  Predicting personality from patterns of behavior collected with smartphones , 2020, Proceedings of the National Academy of Sciences.

[33]  Rui Wang,et al.  Sensing Behavioral Change over Time , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[34]  Hui Wang,et al.  Assessing personality through objective behavioral sensing , 2017, 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII).

[35]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[36]  S. Rothmann,et al.  THE BIG FIVE PERSONALITY DIMENSIONS AND JOB PERFORMANCE , 2003 .

[37]  Paul T. Costa,et al.  Age Trends and Age Norms for the NEO Personality Inventory-3 in Adolescents and Adults , 2005, Assessment.

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

[39]  Wei Shi,et al.  Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification , 2016, ACL.

[40]  S. Gosling,et al.  A very brief measure of the Big-Five personality domains , 2003 .

[41]  Fanglin Chen,et al.  StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones , 2014, UbiComp.