| Abstrakt: |
Psychological well-being is a multidimensional construct and identifying it using a systematic, comprehensive approach offers insights fundamental to critical outcomes. Social networks are valuable resources for research, providing a pragmatic way of generating empirical evidence on psychological well-being based on the textual indicators across different populations. This study analyzed the information on various Reddit social media groups dedicated to mental health. The classes, namely depression, anxiety, bipolar, and SuicideWatch, using the SWMH dataset have been analyzed. The text-based interactions of persons with mental illness have common motifs like negative language and expressions like 'hopelessness,' 'emptiness,' or 'helplessness.' Topic modeling identified recurring themes and subjects that helped classify discernible factors influencing mental health. Classifiers for multiclass classification to classify targeted mental health issues based on users' network behavior and posts were trained and tested to get predictions on context (e.g., MentalBERT) and non-context-based (e.g., LR and NB) models. The MentalBERT model outperformed the other eight baseline models with an average accuracy of 76.70%, which is 4% more than reported in previous studies. Explainable AI was used to examine the trustworthiness of each model, and the explanations were evaluated using the LIME model. Explainability is crucial as mental health data characterizes syndromes, outcomes, disorders, and signs/symptoms exhibiting probabilistic interrelationships with each other. Explanations of these intricate interconnections can assist the extensive research around the model of well-being and interventions intended to improve the human condition and distill positive human functioning. [ABSTRACT FROM AUTHOR] |
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