JMIR Publications

ABSTRACT

Background:

Mental health is a growing concern and can be measured 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 people’s PWB in a non-intrusive way, and the objective of this study is to test the effectiveness of using social media language expression as a predictor of PWB.

Objective:

The study aims to investigate the predictive power of social media corresponding to ground-truth mental health data in a psychological way.

Methods:

We recruited 1,427 participants. Their mental health was evaluated with six dimensions of PWB. Their posts on social media were collected with six psychological lexicons being used to extract linguistic features. A multi-objective prediction model was then built with the extracted linguistic features as input and the 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 coefficient between the predicted PWB scores of social media users and the actual scores obtained by using the linguistic prediction model of this study is 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 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 mental health data from the perspective of PWB. Our study has positive implications for the use of social media to predict mental health in non-professional settings such as self-test or mass censuses.