  Misinformation of COVID-19 is prevalent on social media as the pandemic
unfolds, and the associated risks are extremely high. Thus, it is critical to
detect and combat such misinformation. Recently, deep learning models using
natural language processing techniques, such as BERT (Bidirectional Encoder
Representations from Transformers), have achieved great successes in detecting
misinformation. In this paper, we proposed an explainable natural language
processing model based on DistilBERT and SHAP (Shapley Additive exPlanations)
to combat misinformation about COVID-19 due to their efficiency and
effectiveness. First, we collected a dataset of 984 claims about COVID-19 with
fact checking. By augmenting the data using back-translation, we doubled the
sample size of the dataset and the DistilBERT model was able to obtain good
performance (accuracy: 0.972; areas under the curve: 0.993) in detecting
misinformation about COVID-19. Our model was also tested on a larger dataset
for AAAI2021 - COVID-19 Fake News Detection Shared Task and obtained good
performance (accuracy: 0.938; areas under the curve: 0.985). The performance on
both datasets was better than traditional machine learning models. Second, in
order to boost public trust in model prediction, we employed SHAP to improve
model explainability, which was further evaluated using a between-subjects
experiment with three conditions, i.e., text (T), text+SHAP explanation (TSE),
and text+SHAP explanation+source and evidence (TSESE). The participants were
significantly more likely to trust and share information related to COVID-19 in
the TSE and TSESE conditions than in the T condition. Our results provided good
implications in detecting misinformation about COVID-19 and improving public
trust.
