When phones get personal : predicting big five personality traits from application usage |
|
Author: | Peltonen, Ella1; Sharmila, Parsa1; Opoku Asare, Kennedy1; |
Organizations: |
1Center for Ubiquitous Computing, University of Oulu, P.O. Box 4500, FI 90014, Finland 2Department of Computer Science, University of Helsinki, P.O. Box 64, FI 00014, Finland |
Format: | article |
Version: | published version |
Access: | open |
Online Access: | PDF Full Text (PDF, 0.4 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2020102687660 |
Language: | English |
Published: |
Elsevier,
2020
|
Publish Date: | 2020-10-26 |
Description: |
AbstractAs smartphones are increasingly an integral part of daily life, recent literature suggests a deeper relationship between personality traits and smartphone usage. However, this relationship depends on many complex factors such as geographic location, demographics, or cultural influence, just to name a few. These factors provide crucial knowledge for e.g. usage support, recommendations, marketing, general usage improvements. We use six months of application usage data from 739 Android smartphone user together with the IPIP 50-item Big Five personality traits questionnaire. As our main contribution, we show that even category-level aggregated application usage can predict Big Five traits at up to 86%–96% prediction fit in our sample. Our results show the effect of personality traits on application usage (mean error improvement on random guess 17.0%). We also identify which application usage data best describe the Big Five personality traits. Our work enables future personality-driven research, and shows that when studying personality, application categories can provide sufficient predictions in general traits. see all
|
Series: |
Pervasive and mobile computing |
ISSN: | 1574-1192 |
ISSN-E: | 1873-1589 |
ISSN-L: | 1574-1192 |
Volume: | 69 |
Article number: | 101269 |
DOI: | 10.1016/j.pmcj.2020.101269 |
OADOI: | https://oadoi.org/10.1016/j.pmcj.2020.101269 |
Type of Publication: |
A1 Journal article – refereed |
Field of Science: |
113 Computer and information sciences |
Subjects: | |
Funding: |
This research is supported by Academy of Finland 6Genesis Flagship (grant 318927), SENSATE (grants 316253, 320089), Infotech Emerging Project, and Nokia Foundation (Jorma Ollila Grant for Dr Ella Peltonen). |
Academy of Finland Grant Number: |
318927 316253 320089 |
Detailed Information: |
318927 (Academy of Finland Funding decision) 316253 (Academy of Finland Funding decision) 320089 (Academy of Finland Funding decision) |
Copyright information: |
© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |