CFAH: A Chinese Dataset for Detecting False Advertising in Healthcare

False advertising in healthcare can lead to severe harm to public health and disrupt market order. Traditional regulation of healthcare advertisements relies on manual review, leading to inefficiency. Furthermore, existing detection systems often rely on keyword matching to filter content, which lacks deep semantic analysis of advertising texts and may result in significant under-detection. To tackle this issue, we propose an automatic detection task for false advertising techniques in healthcare including extracting false advertising spans and determining the techniques used. However, there is currently a lack of datasets available for research in this task. In response, we construct and release a Chinese dataset for detecting False Advertising in Healthcare(CFAH), which includes 12 types of false advertising techniques in healthcare advertisements, annotated at the span level. Viewing our task as a span identification challenge, we evaluated mainstream span identification models on the CFAH dataset. The experimental results demonstrate that both span-based traditional methods and fine-tuned generative Large Language Models (LLMs) perform well on this task, with specific models in each category showing the best performance. These results provide important model baselines and references for subsequent research.