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

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...

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Autores principales: Lekkas, Damien, Gyorda, Joseph A., Moen, Erika L., Jacobson, Nicholas C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710841/
https://www.ncbi.nlm.nih.gov/pubmed/36449466
http://dx.doi.org/10.1371/journal.pone.0277516
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author Lekkas, Damien
Gyorda, Joseph A.
Moen, Erika L.
Jacobson, Nicholas C.
author_facet Lekkas, Damien
Gyorda, Joseph A.
Moen, Erika L.
Jacobson, Nicholas C.
author_sort Lekkas, Damien
collection PubMed
description 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.
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spelling pubmed-97108412022-12-01 Using passive sensor data to probe associations of social structure with changes in personality: A synthesis of network analysis and machine learning Lekkas, Damien Gyorda, Joseph A. Moen, Erika L. Jacobson, Nicholas C. PLoS One Research Article 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. Public Library of Science 2022-11-30 /pmc/articles/PMC9710841/ /pubmed/36449466 http://dx.doi.org/10.1371/journal.pone.0277516 Text en © 2022 Lekkas et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lekkas, Damien
Gyorda, Joseph A.
Moen, Erika L.
Jacobson, Nicholas C.
Using passive sensor data to probe associations of social structure with changes in personality: A synthesis of network analysis and machine learning
title Using passive sensor data to probe associations of social structure with changes in personality: A synthesis of network analysis and machine learning
title_full Using passive sensor data to probe associations of social structure with changes in personality: A synthesis of network analysis and machine learning
title_fullStr Using passive sensor data to probe associations of social structure with changes in personality: A synthesis of network analysis and machine learning
title_full_unstemmed Using passive sensor data to probe associations of social structure with changes in personality: A synthesis of network analysis and machine learning
title_short Using passive sensor data to probe associations of social structure with changes in personality: A synthesis of network analysis and machine learning
title_sort using passive sensor data to probe associations of social structure with changes in personality: a synthesis of network analysis and machine learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710841/
https://www.ncbi.nlm.nih.gov/pubmed/36449466
http://dx.doi.org/10.1371/journal.pone.0277516
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