Large Language Model–Assisted Annotation Framework for Cross-Platform Analysis of Online Autism Communities: Implications for Parent Education and Digital Support - Summary - MDSpire
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Large Language Model–Assisted Annotation Framework for Cross-Platform Analysis of Online Autism Communities: Implications for Parent Education and Digital Support
To compare topic structures, poster identity distributions, and help-seeking pathways across different autism-related online health communities (OHCs) in China.
Approach:
Study Design: Examine two representative autism-related platforms: an open forum platform (Baidu Tieba) and physician-patient consultation platforms (Chunyu Doctor and Haodf) using a unified analytical framework.
Key Findings:
More than 10 million people in China are affected by autism-related disorders, with 20% being children.
Online health communities serve as crucial platforms for families seeking information and support.
There is a lack of systematic comparisons across different platform structures within the same disease context.
Interpretation:
The study highlights the importance of understanding the dynamics of online health communities for better parental education and support in autism management.
Limitations:
Previous research has primarily focused on single platforms, particularly in Western contexts.
Limited quantitative analysis of identity stratification and participation hierarchies in open forum platforms.
Conclusion:
The findings aim to enhance understanding of online autism communities and inform parental education and support strategies.
