Automated COVID-19 misinformation checking system using encoder representation with deep learning models

Abstract

Social media impacts society whether these impacts are positive or negative, or even both. It has become a key component of our lives and a vital news resource. The crisis of covid-19 has impacted the lives of all people. The spread of misinformation causes confusion among individuals. So automated methods are vital to detect the wrong arguments to prevent misinformation spread. The covid-19 news can be classified into two categories: false or real. This paper provides an automated misinformation checking system for the covid-19 news. Five machine learning algorithms and deep learning models are evaluated. The proposed system uses the bidirectional encoder representations from transformers (BERT) with deep learning models. detecting fake news using BERT is a fine-tuning. BERT achieved accuracy (98.83%) as a pre-trained and a classifier on the covid-19 dataset. Better results are obtained using BERT with deep learning models (LSTM), which achieved accuracy (99.1%). The results achieved improvements in the area of fake news detection. Another contribution of the proposed system allows users to detect claims' credibility. It finds the most related real news from experts to the fake claims and answers any question about covid-19 using the universal-sentence-encoder model.