Artificial intelligence has achieved notable advances across many applications, and the field is recently
concerned with developing novel methods to explain machine learning models. Deep neural networks
deliver the best performance accuracy in different domains, such as text categorization, image classification,
and speech recognition. Since the neural network models are black-box types, they lack transparency
and explainability in predicting results. During the COVID-19 pandemic, Fake News Detection is a
challenging research problem as it endangers the lives of many online users by providing misinformation.
Therefore, the transparency and explainability of COVID-19 fake news classification are necessary
for building the trustworthiness of model prediction. We proposed an integrated LIME-BiLSTM
model where BiLSTM assures classification accuracy, and LIME ensures transparency and explainability.
In this integrated model, since LIME behaves similarly to the original model and explains the prediction,
the proposed model becomes comprehensible. The performance of this model in terms of explainability
is measured by using Kendall’s tau correlation coefficient. We also employ several machine learning
models and provide a comparison of their performances. Therefore, we analyzed and compared the
computation overhead of our proposed model with the other methods because the model takes the