A New Approach to Detect Fake News Related to Covid-19 Pandemic Using Deep Neural Network - Abdulrahman, Awf; Baykara, Muhammet (Interdisciplinary Publishing Academia, 2022)
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 long-short term memory neural network (LSTM). The second model consists of three layers of a 1D-CNN followed by two layers of 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. © 2022, Interdisciplinary Publishing Academia. All rights reserved.
Keywords: BiLSTM; CNN; Deep learning COVID-19; Fake news detection; LSTM