Exploring mental health literacy on twitter: A machine learning approach

Abstract

Objectives

This study investigates whether reducing mental illness stigma, enhancing help-seeking efficacy, and maintaining positive mental health mediate the relationship between the recognition of mental disorders and help-seeking attitudes.

Methods

During annotation phase, Twitter were collected data from April to August 2022. Tweets were retrieved using keywords aligned with five mental health literacy (MHL) facets: maintaining positive mental health (M), recognizing mental disorders (R), reducing mental illness stigma (S), help-seeking attitude (HA), and help-seeking efficacy (HE). A pretrained Sentence-BERT model generated embedding vectors for classification tasks, achieving 0.85 precision and 0.88 accuracy. Tweets from November 2021 to December 2022 were organized into three time points: R at Time 1; M, S, and HE at Time 2; and HA at Time 3. In total, 4,471,951 tweets from 941 users were analyzed. Structural equation modeling was employed to examine the temporal relationships among MHL components.

Results

Single mediation models indicated that better recognition of mental disorders is associated with more favorable maintenance of positive mental health, greater help-seeking efficacy, and lower mental illness stigma—all of linked to more positive help-seeking attitudes. However, in the multiple mediation model, the reduction of mental illness stigma did not significantly mediate the relationship between the recognition of mental disorders and help-seeking attitudes.

Conclusions

This findings suggest that recognizing mental disorders influences help-seeking attitudes through mediators like help-seeking efficacy and positive mental health maintenance. These results provide valuable insights for future interventions and policies aimed at promoting help-seeking behaviors and advancing mental health literacy.