Abstract
Skepticism from medical professionals has historically played a critical role in identifying early medical warnings, especially during the COVID-19 pandemic. In today’s digital environment, skepticism frequently emerges in online discussions, yet its influence remains underexplored. This study examines how medical professionals’ skepticism affects public discourse in online medical communities. We propose a novel knowledge distillation framework to quantify skepticism in online posts by leveraging LLMs and a domain-specific skepticism detection deep learning model. Empirical analysis shows that skepticism expressed by medical professionals are associated with significantly deeper discussion threads, more replies, and longer-lasting conversations. This study introduces a scalable method for detecting professional skepticism in online discussions and highlights its important role in fostering more sustained and meaningful public discourse.
Recommended Citation
Huang, Jujun; Liu, Rong; and Duan, Wenjing, "Professional Skepticism: A Catalyst for Public Discourse in Online Medical Communities" (2025). Proceedings of the 2025 Pre-ICIS SIGDSA Symposium. 34.
https://aisel.aisnet.org/sigdsa2025/34