JMIR Publications

ABSTRACT

Background:

With the development of online healthcare, more and more patients are consulting and exchanging social support through online health communities. People with different diseases have different needs for information and emotion. However, the comparative analysis of similarities and differences in the behavioral patterns between patients with different disease types and their social support needs requires further exploration.

Objective:

Using a large-scale dataset of user-generated posts, we aims at systematically examining how disease type (acute vs. chronic) influences behavioral patterns, emotional expressions, and support-seeking needs of users in online health communities, providing actionable insights for tailored community interventions.

Methods:

We identified two groups of acute and chronic diseases, then crawled corresponding user profiles and post data from chronic disease online community (CHOHC) and acute disease online community (ACOHC). Using a pre-trained model, we classified and described the users’ social support performance. Subsequently, we conducted a comparative analysis of user behaviors, emotions and needs by mining behavior patterns and textual content from posts. Additionally, we adopt further social network analysis using user profile.

Results:

Base on 492,495 posts of 53,245 users from CHOHC and 52,047 posts of 23,659 users from ACOHC, we find that emotional support seeking and providing are higher in CHOHC (SES 16.92% and PES 20.63%) while ACOHC dominated by informational support seeking and providing (SIS 22.82% and PIS 41.00%), indicating that chronic disease users have a higher need for emotional support while most of the acute disease users want to seek informational support. The word co-occurrence network revealed distinct thematic patterns between the two communities. In CHOHC, disease management (47%) clusters and emotional clusters (41%) appeared in balanced proportions, reflecting the dual needs of chronic disease patients. In contrast, ACOHC posts were overwhelmingly treatment-focused (89%), with minimal emotional vocabulary (7%). Social network analysis further highlighted these differences. CHOHC showed the highest edge density in SES subnetworks and reciprocal interactions in 68% of PES connections, indicating robust emotional support exchanges. Meanwhile, ACOHC exhibited significantly faster post velocity in treatment discussions, consistent with its acute care context. These structural differences aligned with user behavior patterns: chronic disease users maintained strong community bonds (averaging 8.2 connections/user), while acute disease users prioritized time-sensitive information (with 92% of queries being treatment-related).

Conclusions:

This research contribute to comprehensive understanding of how disease type influence users social behavior and emotional expressions. The findings provide practical implications for doctors, patients’ families, and healthcare participants regarding targeted support strategies for patients with acute and chronic diseases.