دورية أكاديمية
أعرض في EDS

Using passive sensor data to probe associations of social structure with changes in personality: A synthesis of network analysis and machine learning.

التفاصيل البيبلوغرافية
العنوان: Using passive sensor data to probe associations of social structure with changes in personality: A synthesis of network analysis and machine learning.
المؤلفون: Lekkas D; Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire, United States of America.; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, New Hampshire, United States of America., Gyorda JA; Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire, United States of America.; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, New Hampshire, United States of America., Moen EL; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, New Hampshire, United States of America.; Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire, United States of America., Jacobson NC; Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire, United States of America.; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, New Hampshire, United States of America.; Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire, United States of America.; Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, United States of America.
المصدر: PloS one [PLoS One] 2022 Nov 30; Vol. 17 (11), pp. e0277516. Date of Electronic Publication: 2022 Nov 30 (Print Publication: 2022).
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science
مواضيع طبية MeSH: Social Structure* , Personality Disorders*, Humans ; Personality ; Individuality ; Machine Learning
مستخلص: Social network analysis (SNA) is an increasingly popular and effective tool for modeling psychological phenomena. Through application to the personality literature, social networks, in conjunction with passive, non-invasive sensing technologies, have begun to offer powerful insight into personality state variability. Resultant constructions of social networks can be utilized alongside machine learning-based frameworks to uniquely model personality states. Accordingly, this work leverages data from a previously published study to combine passively collected wearable sensor information on face-to-face, workplace social interactions with ecological momentary assessments of personality state. Data from 54 individuals across six weeks was used to explore the relative importance of 26 unique structural and nodal social network features in predicting individual changes in each of the Big Five (5F) personality states. Changes in personality state were operationalized by calculating the weekly root mean square of successive differences (RMSSD) in 5F state scores measured daily via self-report. Using only SNA-derived features from wearable sensor data, boosted tree-based machine learning models explained, on average, approximately 28-30% of the variance in individual personality state change. Model introspection implicated egocentric features as the most influential predictors across 5F-specific models, with network efficiency, constraint, and effective size measures among the most important. Feature importance profiles for each 5F model partially echoed previous empirical findings. Results support future efforts focusing on egocentric components of SNA and suggest particular investment in exploring efficiency measures to model personality fluctuations within the workplace setting.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2022 Lekkas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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معلومات مُعتمدة: P30 DA029926 United States DA NIDA NIH HHS
تواريخ الأحداث: Date Created: 20221130 Date Completed: 20221202 Latest Revision: 20230130
رمز التحديث: 20240829
مُعرف محوري في PubMed: PMC9710841
DOI: 10.1371/journal.pone.0277516
PMID: 36449466
قاعدة البيانات: MEDLINE
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0277516