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“Please Help Me!” Using Large Language Models to Improve Titles of User-Generated Posts in Online Health Communities

In: People, Society, and Ethical Challenges of Information Systems

Author

Listed:
  • Johannes Chen

    (Goethe University Frankfurt, Chair of Information Systems and Information Management)

Abstract

In online health communities (OHC), users can post questions to seek health-related advice from healthcare professionals. However, the titles they formulate often lack key information. Given that many people only scan titles, users may not get their questions answered. Large language models (LLM) offer a potential solution by generating titles that better align with the information needs of healthcare professionals. In this study, we fine-tuned an LLM using over 330.000 posts from the subreddit r/askdocs. Subsequently, we conducted a survey with 70 healthcare professionals to evaluate their preference between user- and LLM-generated titles. Our findings indicate that healthcare professionals perceive LLM-generated titles as better suited to the corresponding posts, more informative, and conveying a greater sense of urgency. With our work, we contribute to research on OHCs and LLMs by demonstrating that LLMs can improve the titles of user-generated posts compared to those generated by users themselves.

Suggested Citation

  • Johannes Chen, 2026. "“Please Help Me!” Using Large Language Models to Improve Titles of User-Generated Posts in Online Health Communities," Lecture Notes in Information Systems and Organization, in: Christoph M. Flath & Gunther Gust & Frédéric Thiesse & Axel Winkelmann (ed.), People, Society, and Ethical Challenges of Information Systems, pages 383-395, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-08486-6_26
    DOI: 10.1007/978-3-032-08486-6_26
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