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Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study

BACKGROUND: Positive mental health is arguably increasingly important and can be revealed, to some extent, in terms of psychological well-being (PWB). However, PWB is difficult to assess in real time on a large scale. The popularity and proliferation of social media make it possible to sense and mon...

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Detalles Bibliográficos
Autores principales: Han, Nuo, Li, Sijia, Huang, Feng, Wen, Yeye, Wang, Xiaoyang, Liu, Xiaoqian, Li, Linyan, Zhu, Tingshao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9929724/
https://www.ncbi.nlm.nih.gov/pubmed/36719723
http://dx.doi.org/10.2196/41823
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author Han, Nuo
Li, Sijia
Huang, Feng
Wen, Yeye
Wang, Xiaoyang
Liu, Xiaoqian
Li, Linyan
Zhu, Tingshao
author_facet Han, Nuo
Li, Sijia
Huang, Feng
Wen, Yeye
Wang, Xiaoyang
Liu, Xiaoqian
Li, Linyan
Zhu, Tingshao
author_sort Han, Nuo
collection PubMed
description BACKGROUND: Positive mental health is arguably increasingly important and can be revealed, to some extent, in terms of psychological well-being (PWB). However, PWB is difficult to assess in real time on a large scale. The popularity and proliferation of social media make it possible to sense and monitor online users’ PWB in a nonintrusive way, and the objective of this study is to test the effectiveness of using social media language expression as a predictor of PWB. OBJECTIVE: This study aims to investigate the predictive power of social media corresponding to ground truth well-being data in a psychological way. METHODS: We recruited 1427 participants. Their well-being was evaluated using 6 dimensions of PWB. Their posts on social media were collected, and 6 psychological lexicons were used to extract linguistic features. A multiobjective prediction model was then built with the extracted linguistic features as input and PWB as the output. Further, the validity of the prediction model was confirmed by evaluating the model's discriminant validity, convergent validity, and criterion validity. The reliability of the model was also confirmed by evaluating the split-half reliability. RESULTS: The correlation coefficients between the predicted PWB scores of social media users and the actual scores obtained using the linguistic prediction model of this study were between 0.49 and 0.54 (P<.001), which means that the model had good criterion validity. In terms of the model’s structural validity, it exhibited excellent convergent validity but less than satisfactory discriminant validity. The results also suggested that our model had good split-half reliability levels for every dimension (ranging from 0.65 to 0.85; P<.001). CONCLUSIONS: By confirming the availability and stability of the linguistic prediction model, this study verified the predictability of social media corresponding to ground truth well-being data from the perspective of PWB. Our study has positive implications for the use of social media to predict mental health in nonprofessional settings such as self-testing or a large-scale user study.
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spelling pubmed-99297242023-02-16 Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study Han, Nuo Li, Sijia Huang, Feng Wen, Yeye Wang, Xiaoyang Liu, Xiaoqian Li, Linyan Zhu, Tingshao J Med Internet Res Original Paper BACKGROUND: Positive mental health is arguably increasingly important and can be revealed, to some extent, in terms of psychological well-being (PWB). However, PWB is difficult to assess in real time on a large scale. The popularity and proliferation of social media make it possible to sense and monitor online users’ PWB in a nonintrusive way, and the objective of this study is to test the effectiveness of using social media language expression as a predictor of PWB. OBJECTIVE: This study aims to investigate the predictive power of social media corresponding to ground truth well-being data in a psychological way. METHODS: We recruited 1427 participants. Their well-being was evaluated using 6 dimensions of PWB. Their posts on social media were collected, and 6 psychological lexicons were used to extract linguistic features. A multiobjective prediction model was then built with the extracted linguistic features as input and PWB as the output. Further, the validity of the prediction model was confirmed by evaluating the model's discriminant validity, convergent validity, and criterion validity. The reliability of the model was also confirmed by evaluating the split-half reliability. RESULTS: The correlation coefficients between the predicted PWB scores of social media users and the actual scores obtained using the linguistic prediction model of this study were between 0.49 and 0.54 (P<.001), which means that the model had good criterion validity. In terms of the model’s structural validity, it exhibited excellent convergent validity but less than satisfactory discriminant validity. The results also suggested that our model had good split-half reliability levels for every dimension (ranging from 0.65 to 0.85; P<.001). CONCLUSIONS: By confirming the availability and stability of the linguistic prediction model, this study verified the predictability of social media corresponding to ground truth well-being data from the perspective of PWB. Our study has positive implications for the use of social media to predict mental health in nonprofessional settings such as self-testing or a large-scale user study. JMIR Publications 2023-01-31 /pmc/articles/PMC9929724/ /pubmed/36719723 http://dx.doi.org/10.2196/41823 Text en ©Nuo Han, Sijia Li, Feng Huang, Yeye Wen, Xiaoyang Wang, Xiaoqian Liu, Linyan Li, Tingshao Zhu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.01.2023. 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 work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Han, Nuo
Li, Sijia
Huang, Feng
Wen, Yeye
Wang, Xiaoyang
Liu, Xiaoqian
Li, Linyan
Zhu, Tingshao
Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study
title Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study
title_full Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study
title_fullStr Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study
title_full_unstemmed Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study
title_short Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study
title_sort sensing psychological well-being using social media language: prediction model development study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9929724/
https://www.ncbi.nlm.nih.gov/pubmed/36719723
http://dx.doi.org/10.2196/41823
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