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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Framework for Annotation Utilizing Large Language Models to Analyze Online Autism Communities Across Platforms: Insights for Parental Education and Digital Support

  • By

  • Yifan Xu

  • Jianhao Ma

  • Yujia Hu

  • Yixue Liu

  • Yu Chen

  • Wei Feng

  • Changwei Zhang

  • Lei Zhang

  • Xuening Zhang

  • Ruochen Huang

  • July 10, 2026

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Objective:

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.

Original Source(s)

Large Language Model–Assisted Annotation Framework for Cross-Platform Analysis of Online Autism Communities: Implications for Parent Education and Digital Support

  • by Yifan Xu, Jianhao Ma, Yujia Hu, Yixue Liu, Yu Chen, Wei Feng, Changwei Zhang, Lei Zhang, Xuening Zhang, Ruochen Huang

  • July 10, 2026

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