Imperial College London
Portrait Picture

Professor Aldo Faisal

Faculty of Engineering, Department of Bioengineering

Professor of AI & Neuroscience
 
 
 
//

Contact

 

+44 (0)20 7594 6373a.faisal Website

 
 
//

Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
//

Location

 

4.08Royal School of MinesSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Khwaja:2019:10.1145/3351246,
author = {Khwaja, M and Vaid, SS and Zannone, S and Harari, GM and Faisal, A and Matic, A},
doi = {10.1145/3351246},
journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
pages = {1--24},
title = {Modeling personality vs. modeling  personalidad: In-the-wild mobile data analysis in five countries suggests cultural impact on personality models},
url = {http://dx.doi.org/10.1145/3351246},
volume = {3},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Sensor data collected from smartphones provides the possibility to passively infer a user’s personality traits. Such models canbe used to enable technology personalization, while contributing to our substantive understanding of how human behaviormanifests in daily life. A significant challenge in personality modeling involves improving the accuracy of personalityinferences, however, research has yet to assess and consider the cultural impact of users’ country of residence on modelreplicability. We collected mobile sensing data and self-reported Big Five traits from 166 participants (54 women and 112men) recruited in five different countries (UK, Spain, Colombia, Peru, and Chile) for 3 weeks. We developed machine learningbased personality models using culturally diverse datasets - representing different countries - and we show that such modelscan achieve state-of-the-art accuracy when tested in new countries, ranging from 63% (Agreeableness) to 71% (Extraversion)of classification accuracy. Our results indicate that using country-specific datasets can improve the classification accuracybetween 3% and 7% for Extraversion, Agreeableness, and Conscientiousness. We show that these findings hold regardless ofgender and age balance in the dataset. Interestingly, using gender- or age- balanced datasets as well as gender-separateddatasets improve trait prediction by up to 17%. We unpack differences in personality models across the five countries, highlightthe most predictive data categories (location, noise, unlocks, accelerometer), and provide takeaways to technologists andsocial scientists interested in passive personality assessment.
AU  - Khwaja,M
AU  - Vaid,SS
AU  - Zannone,S
AU  - Harari,GM
AU  - Faisal,A
AU  - Matic,A
DO  - 10.1145/3351246
EP  - 24
PY  - 2019///
SN  - 2474-9567
SP  - 1
TI  - Modeling personality vs. modeling  personalidad: In-the-wild mobile data analysis in five countries suggests cultural impact on personality models
T2  - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
UR  - http://dx.doi.org/10.1145/3351246
UR  - https://dl.acm.org/citation.cfm?doid=3361560.3351246
UR  - http://hdl.handle.net/10044/1/73023
VL  - 3
ER  -
Download