  As people increasingly prioritize their health, the speed and breadth of
health information dissemination on the internet have also grown. At the same
time, the presence of false health information (health rumors) intermingled
with genuine content poses a significant potential threat to public health.
However, current research on Chinese health rumors still lacks a large-scale,
public, and open-source dataset of health rumor information, as well as
effective and reliable rumor detection methods. This paper addresses this gap
by constructing a dataset containing 1.12 million health-related rumors
(HealthRCN) through web scraping of common health-related questions and a
series of data processing steps. HealthRCN is the largest known dataset of
Chinese health information rumors to date. Based on this dataset, we propose
retrieval-augmented large language models for Chinese health rumor detection
and explainability (HRDE). This model leverages retrieved relevant information
to accurately determine whether the input health information is a rumor and
provides explanatory responses, effectively aiding users in verifying the
authenticity of health information. In evaluation experiments, we compared
multiple models and found that HRDE outperformed them all, including
GPT-4-1106-Preview, in rumor detection accuracy and answer quality. HRDE
achieved an average accuracy of 91.04% and an F1 score of 91.58%.
