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

Autor: Nuo Han, Sijia Li, Feng Huang, Yeye Wen, Xiaoyang Wang, Xiaoqian Liu, Linyan Li, Tingshao Zhu
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Journal of Medical Internet Research, Vol 25, p e41823 (2023)
Druh dokumentu: article
ISSN: 1438-8871
DOI: 10.2196/41823
Popis: BackgroundPositive 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. ObjectiveThis study aims to investigate the predictive power of social media corresponding to ground truth well-being data in a psychological way. MethodsWe 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. ResultsThe 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
Databáze: Directory of Open Access Journals
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