COVID-19 Fake News Detection System

This article deals with the problem of the rapidly increasing COVID-19 infodemic in the world. Thus, there is a need for an effective framework of detecting fake information or misleading news related to COVID-19 virus/disease. To resolve this, we have used a dataset obtained from ConstraintAI'21. The dataset consists of 10,700 tweets and online posts of fake and real news concerning COVID-19. Machine Learning (ML) algorithms compared in this paper to classify the given news or tweet into real or fake are Logistic Regression (LR), K-Nearest Neighbor (KNN), Linear Support Vector Machine (LSVM), Random Forest Classifier (RFC), Decision Tree (DT), Naive Bayes (NB) and Stochastic Gradient Descent (SGD) algorithm. Two feature extraction techniques were used count vectorization and TF-IDF. Deep Learning (DL) algorithms implemented using Adam optimizer are Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The best testing accuracy was achieved with the LSVM model using TF-IDF feature extraction method followed by Stochastic Gradient Descent classifier with TF-IDF feature extraction technique. LR, DT, and RFC performed better with the Count vectorization feature extraction technique, whereas LSVM, KNN, NB and SGD had better accuracy with TF-IDF feature extraction technique. The LSTM model performed slightly better among the DL algorithms.