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. 2023 Jan 31:25:e41823.
doi: 10.2196/41823.

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

Nuo Han  1   2   3 , Sijia Li  4 , Feng Huang  1   2 , Yeye Wen  5 , Xiaoyang Wang  1 , Xiaoqian Liu  1 , Linyan Li  3   6 , Tingshao Zhu  1   2
Affiliations

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

Nuo Han et al. J Med Internet Res. .

Abstract

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.

Keywords: domain knowledge; ground truth; lexicon; linguistic; machine learning; mental health; mental well being; mental wellbeing; model; predict; psychological well-being; social media.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Prenormalized scores: (A) environment mastery, (B) positive relations, (C) autonomy item, (D) personal growth, (E) self-acceptance, (F) purpose in life.
Figure 2
Figure 2
Normalized scores: (A) environment mastery, (B) positive relations, (C) autonomy item, (D) personal growth, (E) self-acceptance, (F) purpose in life.
Figure 3
Figure 3
Information collection process via the online experiment platform. PWB: psychological well-being.
Figure 4
Figure 4
Data preprocessing, feature extraction, and model training. PWB: psychological well-being.
Figure 5
Figure 5
Data preservation rules and model evaluation.
See this image and copyright information in PMC

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