Covid-19 fake news sentiment analysis. (July 2022) - Iwendi, Celestine; Mohan, Senthilkumar; khan, Suleman; Ibeke, Ebuka; Ahmadian, Ali; Ciano, Tiziana
Abstract: 'Fake news' refers to the misinformation presented about issues or events, such as COVID-19. Meanwhile, social media giants claimed to take COVID-19 related misinformation seriously, however, they have been ineffectual. This research uses Information Fusion to obtain real news data from News Broadcasting, Health, and Government websites, while fake news data are collected from social media sites. 39 features were created from multimedia texts and used to detect fake news regarding COVID-19 using state-of-the-art deep learning models. Our model's fake news feature extraction improved accuracy from 59.20% to 86.12%. Overall high precision is 85% using the Recurrent Neural Network (RNN) model; our best recall and F1-Measure for fake news were 83% using the Gated Recurrent Units (GRU) model. Similarly, precision, recall, and F1-Measure for real news are 88%, 90%, and 88% using the GRU, RNN, and Long short-term memory (LSTM) model, respectively. Our model outperformed standard machine learning algorithms. Graphical abstract: Highlights: The research uses the process of Information Fusion to obtain real news data. We have used Deep Learning classifiers to propose 39 novel features for text. The paper proposes sentiment features, linguistic features and name entity base features. After extracting features from the text, our new features detect COVID-19 related fake news with an accuracy of 86.12%. Thus, accuracy is increased by 20% with the novel features. This paper compares Deep Learning algorithm results with the standard Machine Learning (ML) algorithms using the same fusion of dataset and shows a higher accuracy. Therefore, it confirms the superiority of our Deep Learning model.