Between the Lines: Investigating Health Beliefs and Emotional Expressions in Online Mental Health Communities

Khanh Bao Nguyen¹Binh Vu¹Swati Chandna¹Jobst-Hendrik Schultz²Gwendolyn Mayer^2*

• ¹Department of Applied Data Science and Analytics, SRH Hochschule Heidelberg, Heidelberg, Baden-Wurttemberg, Germany
• ²Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg, Germany

This research investigates the application of computational techniques to analyze health-related discussions on Reddit within the framework of the Health Belief Model (HBM). The primary objective is to develop an efficient methodology not only for classifying key HBM components—such as perceived susceptibility, severity, benefits, barriers, cues to action, and self-efficacy—but also for examining emotional expressions within these discussions. Multiple models were tested in classification tasks, with DistilBERT outper-forming other approaches, achieving accuracy rates between 75% and 84% for most components. However, challenges persist in predicting perceived severity, with an accuracy of only 47% due to its multi-label nature; to address this, GPT-4-based key-word extraction was combined with human review, improving accuracy to 81%. Data analysis utilized visualization techniques—such as word clouds, heatmaps, and emotional content analysis—to identify thematic trends and emotional expres-sions in the discussions. It reveals pat-terns in mental health discussions, such as the attribution of personality as a root cause of anxiety by users and the urgent need for targeted interventions in cases of suicidal ideation. Future work should prioritize improving model adaptability to health-specific data, handling rare terms, conducting nuanced emotional analyses in written expressions, and addressing ethical implications in analyzing user-generated content.