@article{Ibeke_E-2022_66357,
title = {Covid-19 fake news sentiment analysis.},
author = {Ibeke, E. and Iwendi, C. and Mohan, S. and Khan, S. and Ahmadian, A. and Ciano, T.},
month = {feb},
year = {2022},
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 the 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.},
journal = {Computers and electrical engineering},
publisher = {Elsevier},
url = {https://publichealthscotland.scot/id/66357},
}
