Combat COVID-19 infodemic using explainable natural language processing models

作者：

Highlights：

• Natural language processing models based on DistilBERT are able to detect misinformation of COVID-19.

• Explanations based on SHAP are able to improve user trust in model prediction.

• With extra explanations on claims of COVID-19, users are more willing to share them.

• It is important not only to detect misinformation but also to explain why it is fake or true.

摘要

•Natural language processing models based on DistilBERT are able to detect misinformation of COVID-19.•Explanations based on SHAP are able to improve user trust in model prediction.•With extra explanations on claims of COVID-19, users are more willing to share them.•It is important not only to detect misinformation but also to explain why it is fake or true.

论文关键词：COVID-19,Misinformation detection,Trust,BERT,DistilBERT,SHAP

论文评审过程：Received 5 November 2020, Revised 25 February 2021, Accepted 28 February 2021, Available online 6 March 2021, Version of Record 20 March 2021.

论文官网地址：https://doi.org/10.1016/j.ipm.2021.102569