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%.

翻译：随着人们对健康的日益重视，互联网上健康信息的传播速度和广度也随之增长。与此同时，虚假健康信息（健康谣言）与真实内容混杂存在，对公众健康构成了重大的潜在威胁。然而，当前针对中文健康谣言的研究仍缺乏大规模、公开、开源的健康谣言信息数据集，以及有效可靠的谣言检测方法。本文通过抓取常见健康相关问题并经过一系列数据处理步骤，构建了一个包含112万条健康相关谣言的数据集（HealthRCN），以填补这一空白。HealthRCN是迄今为止已知规模最大的中文健康信息谣言数据集。基于此数据集，我们提出了用于中文健康谣言检测与可解释性的检索增强大型语言模型（HRDE）。该模型利用检索到的相关信息，准确判断输入的健康信息是否为谣言，并提供解释性回答，有效帮助用户验证健康信息的真实性。在评估实验中，我们比较了多种模型，发现HRDE在谣言检测准确率和答案质量上均优于所有对比模型，包括GPT-4-1106-Preview。HRDE的平均准确率达到91.04%，F1分数为91.58%。