A New Approach to Detect Fake News Related to Covid-19 Pandemic Using Deep Neural Network
The fake news that accompanied the COVID-19 pandemic on social media platforms negatively affected people and led to a state of panic and fear of the unknown. This study aims to build a model for classifying textual news for four datasets related to COVID-19, binary classification (fake and real) with high performance. Two hybrid deep learning models were built. The first model consists of three layers of a one-dimension convolutional neural network (1D-CNN), followed by two layers of a long-short-term memory neural network (LSTM). The second model consists of three layers of a 1D-CNN followed by two layers of a bidirectional LSTM neural network (BiLSTM). Finally, the results obtained using hybrid models were compared with the results obtained by applying three machine learning classifiers (naïve Bayes, logistic regression, and k-nearest neighbor) on the same data sets. This study achieved promising results with an accuracy of (96.98%, 94.52%, 99.60%, and 99.90%) for the first model with all data sets and (97.15%, 95.32%, 99.40%, and 99.82%) for the second model with the same four data sets.
Fake news detection, BiLSTM, Deep learning COVID-19, LSTM, CNN
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