关键词:

虚假信息识别, 虚假医疗信息, BERT模型 , 深度学习, 在线医疗信息

Abstract:

[Purpose/significance]This research aims to solve the problem of insufficient professional knowledge when obtaining false online medical information data sets, and helps build false online medical information recognition models in the field of small samples；[Method/process] we propose an idea of constructing an online fake medical information dataset based on the transformed extraction of authoritative misinformation refuting, and construct the traditional machine learning model, CNN model and BERT model for recognition；[Result/conclusion] The results show that the construction of an online false medical information dataset based on misinformation refuting can be achieved at a lower cost and without relying on expert labeling. The comparative experiments based on the data set of online false medical information related to the COVID-19 show that the accuracy rate of the BERT model pre-trained based on Weibo data is 95.91%, and the F1 value is 94.57%, which is compared with traditional machine learning models and CNN model the increase is close to 6% and 4% respectively. It means that the pre-trained BERT-based model constructed in this paper achieves better results on the detection of false online medical information task.

Key words:

Misinformation detection, False medical information, BERT model; Deep learning, Online medical information