ArticleProceedings of the National Academy of Sciences of the United States of America2020
Predicting personality from patterns of behavior collected with smartphones.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 42 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
42 citing papers in PubMed, 302 citations in OpenAlex.
- Does true crime increase negative affect, or negative affect lead to true crime? A cross-lagged analysis on the dynamics of consuming frightening media.BMC psychology · 2026Article
- Personality Constructs Predictions Beyond FFM/Big5: A Digital Phenotyping-Based Exploration.Journal of personality · 2026Article
- Exploring individual differences in volitional personality state and trait change: The role of motivation and engagement during a 12-week intervention.European journal of personality · 2026Article
- Computer-Assisted Performance-Based Assessment for Mental Health: A Scoping Review.PsyCh journal · 2026Article
- Assessing personality using zero-shot generative AI scoring of brief open-ended text.Nature human behaviour · 2026Article
- Deep Beats, Deep Thoughts? Predicting General Cognitive Ability from Natural Music-Listening Behavior.Journal of Intelligence · 2026Article
- Article
- Construction of a personality trait prediction model based on color perception preference and personalized cognitive intervention pathways in art education.Frontiers in psychology · 2026Article
- From direct scoring to cue integration: big five prediction from spoken responses to a picture-based self-projection task.Frontiers in psychology · 2026Article
- Digital Phenotyping of Sensation Seeking: A Machine Learning Approach Using Gait Analysis.Behavioral sciences (Basel, Switzerland) · 2025Article
- The analysis of marketing performance in E-commerce live broadcast platform based on big data and deep learning.Scientific reports · 2025Article
- Insights into the Technological Evolution and Research Trends of Mobile Health: Bibliometric Analysis.Healthcare (Basel, Switzerland) · 2025Review
- Novel Profiles of Family Media Use: Latent Profile Analysis.JMIR pediatrics and parenting · 2025Article
- Article
- Mobility-Based Smartphone Digital Phenotypes for Unobtrusively Capturing Everyday Cognition, Mood, and Community Life-Space in Older Adults: Feasibility, Acceptability, and Preliminary Validity Study.JMIR human factors · 2024Article
- Effects of lockdowns in shaping socioeconomic behaviors.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Personalized mood prediction from patterns of behavior collected with smartphones.NPJ digital medicine · 2024Article
- Job characteristics and personality change in young adulthood: A 12-year longitudinal study and replication.Journal of personality · 2024Article
- Opinion: Which animals have personality?Personality neuroscience · 2024Review
- Evaluating longitudinal relationships between parental monitoring and substance use in a multi-year, intensive longitudinal study of 670 adolescent twins.Frontiers in psychiatry · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Smartphones enjoy high adoption rates around the globe. Rarely more than an arm's length away, these sensor-rich devices can easily be repurposed to collect rich and extensive records of their users' behaviors (e.g., location, communication, media consumption), posing serious threats to individual privacy. Here we examine the extent to which individuals' Big Five personality dimensions can be predicted on the basis of six different classes of behavioral information collected via sensor and log data harvested from smartphones. Taking a machine-learning approach, we predict personality at broad domain ([Formula: see text] = 0.37) and narrow facet levels ([Formula: see text] = 0.40) based on behavioral data collected from 624 volunteers over 30 consecutive days (25,347,089 logging events). Our cross-validated results reveal that specific patterns in behaviors in the domains of 1) communication and social behavior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day- and night-time activity are distinctively predictive of the Big Five personality traits. The accuracy of these predictions is similar to that found for predictions based on digital footprints from social media platforms and demonstrates the possibility of obtaining information about individuals' private traits from behavioral patterns passively collected from their smartphones. Overall, our results point to both the benefits (e.g., in research settings) and dangers (e.g., privacy implications, psychological targeting) presented by the widespread collection and modeling of behavioral data obtained from smartphones.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.