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Sensing Behavioral Change over Time: Using Within-Person Variability Features from Mobile Sensing to Predict Personality Traits

Authors:

Gabriella M. Harari,

Sandrine R. Müller,

Shayan Mirjafari,

Andrew T. CampbellAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 3

Article No.: 141, Pages 1 - 21

https://doi.org/10.1145/3264951

Published: 18 September 2018 Publication History

Abstract

Personality traits describe individual differences in patterns of thinking, feeling, and behaving ("between-person" variability). But individuals also show changes in their own patterns over time ("within-person" variability). Existing approaches to measuring within-person variability typically rely on self-report methods that do not account for fine-grained behavior change patterns (e.g., hour-by-hour). In this paper, we use passive sensing data from mobile phones to examine the extent to which within-person variability in behavioral patterns can predict self-reported personality traits. Data were collected from 646 college students who participated in a self-tracking assignment for 14 days. To measure variability in behavior, we focused on 5 sensed behaviors (ambient audio amplitude, exposure to human voice, physical activity, phone usage, and location data) and computed 4 within-person variability features (simple standard deviation, circadian rhythm, regularity index, and flexible regularity index). We identified a number of significant correlations between the within-person variability features and the self-reported personality traits. Finally, we designed a model to predict the personality traits from the within-person variability features. Our results show that we can predict personality traits with good accuracy. The resulting predictions correlate with self-reported personality traits in the range of r = 0.32, MAE = 0.45 (for Openness in iOS users) to r = 0.69, MAE = 0.55 (for Extraversion in Android users). Our results suggest that within-person variability features from smartphone data has potential for passive personality assessment.

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Cited By

Bhattacharya SYadav GJha AMoorthy VM PChoudhary SKumar S(2024)PIPS: Personality Insights and Prediction using Smartphone Non-Private Content2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)10.1109/CONECCT62155.2024.10677019(1-6)Online publication date: 12-Jul-2024
https://doi.org/10.1109/CONECCT62155.2024.10677019
Juchem CBaumgardt SAsselmann E(2024)Personality-Based Approaches to Health and Health Behavior InterventionsHandbook of Concepts in Health, Health Behavior and Environmental Health10.1007/978-981-97-0821-5_13-1(1-25)Online publication date: 13-Aug-2024
https://doi.org/10.1007/978-981-97-0821-5_13-1
Cao YRajendran SSundararajan PLaw RBacon SSumner SMasuda N(2023)Web-Based Social Networks of Individuals With Adverse Childhood Experiences: Quantitative StudyJournal of Medical Internet Research10.2196/4517125(e45171)Online publication date: 30-May-2023
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Index Terms

Sensing Behavioral Change over Time: Using Within-Person Variability Features from Mobile Sensing to Predict Personality Traits
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
2. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 3

September 2018

1536 pages

EISSN:2474-9567

DOI:10.1145/3279953

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Copyright © 2018 ACM.

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Publication History

Published: 18 September 2018

Accepted: 01 September 2018

Revised: 01 May 2018

Received: 01 November 2017

Published in IMWUT Volume 2, Issue 3

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https://doi.org/10.1109/CONECCT62155.2024.10677019
Juchem CBaumgardt SAsselmann E(2024)Personality-Based Approaches to Health and Health Behavior InterventionsHandbook of Concepts in Health, Health Behavior and Environmental Health10.1007/978-981-97-0821-5_13-1(1-25)Online publication date: 13-Aug-2024
https://doi.org/10.1007/978-981-97-0821-5_13-1
Cao YRajendran SSundararajan PLaw RBacon SSumner SMasuda N(2023)Web-Based Social Networks of Individuals With Adverse Childhood Experiences: Quantitative StudyJournal of Medical Internet Research10.2196/4517125(e45171)Online publication date: 30-May-2023
https://doi.org/10.2196/45171
Wang WXu WChander ANepal SBuck BPakhomov SCohen TBen-Zeev DCampbell A(2023)The Power of Speech in the WildProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108907:3(1-29)Online publication date: 27-Sep-2023
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