JMIR Publications

ABSTRACT

Background:

Online Health Communities (OHCs) are crucial resources for individuals managing socially stigmatized and physically burdensome conditions, such as hemorrhoids. These anonymous forums provide critical avenues for emotional validation and information exchange that may be underrepresented or difficult to access in traditional clinical settings. Despite their growing importance as data sources for understanding patient experiences, large-scale, longitudinal analyses of the thematic and emotional evolution within such communities remain scarce.

Objective:

This study aims to conduct a comprehensive, decade-long (2013–2022) computational language analysis of the r/hemorrhoids Reddit community to map its thematic evolution, characterize its emotional landscape, and determine how expressed sentiment influences community engagement.

Methods:

We collected 9,954 Reddit posts, which were refined through preprocessing into a final dataset of 3,746 high-quality posts. Our methodology involved a multi-stage computational approach: 1) Topic Modeling was performed on the mature phase of the community (2019–2022) using BERTopic to identify stable thematic clusters. 2) Fine-grained emotion classification employed a DistilRoBERTa-based transformer model (j-hartmann/emotion-english-distilroberta-base) to categorize posts into seven discrete emotional states (Neutrality, Fear, Sadness, Joy, Anger, Surprise, Disgust). 3) Longitudinal Analysis tracked the evolution of key themes and the emotional distribution across the community's lifespan, correlating emotional categories with engagement metrics (post score and comment count).

Results:

The analysis revealed a polarized emotional distribution dominated by Neutrality (33.29%) and high levels of Negative Affect (Fear and Disgust). Thematic modeling identified four higher-order thematic domains encompassing 19 semantically coherent topics: (I) Symptom Recognition & Diagnosis, (II) Conservative Treatment & Self-Management, (III) Medical & Surgical Interventions, and (IV) Quality of Life, Risk Factors, & Psychosocial Impact. Notably, posts expressing Fear and Sadness elicited the highest mean comment counts (mean > 7.00), substantially outperforming neutral or positive posts. This indicates that negative emotional expression serves as a potent catalyst for receiving social and empathic support. The community's discourse evolved from a nascent platform of general complaints to a Collective Illness Trajectory Knowledge Base, which accumulated through longitudinal user exchanges that collectively mapped the typical stages of the condition.

Conclusions:

The community functions as an indispensable digital infrastructure for patients navigating a stigmatized condition, validating and managing profound psychological burdens. Contrary to conventional expectations, our findings show that in the context of health stigma, openly expressing negative emotions like fear and sadness emerges as the most effective way to mobilize strong community support. These insights are vital for developing targeted digital health interventions and improving clinical communication strategies to address patients' unarticulated anxieties.