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Covid-19 fake news sentiment analysis.

Iwendi, Celestine; Mohan, Senthilkumar; khan, Suleman; Ibeke, Ebuka; Ahmadian, Ali; Ciano, Tiziana

Authors

Celestine Iwendi

Senthilkumar Mohan

Suleman khan

Ali Ahmadian

Tiziana Ciano



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.

Citation

IWENDI, C., MOHAN, S., KHAN, S., IBEKE, E., AHMADIAN, A. and CIANO, T. 2022. COVID-19 fake news sentiment analysis. Computers and electrical engineering [online], 101, article 107967. Available from: https://doi.org/10.1016/j.compeleceng.2022.107967

Journal Article Type Article
Acceptance Date Mar 28, 2022
Online Publication Date Apr 22, 2022
Publication Date Jul 31, 2022
Deposit Date Mar 3, 2022
Publicly Available Date Apr 23, 2023
Journal Computers and electrical engineering
Print ISSN 0045-7906
Electronic ISSN 1879-0755
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 101
Article Number 107967
DOI https://doi.org/10.1016/j.compeleceng.2022.107967
Keywords Deep learning; Algorithm; Standard machine learning (ML); Fake news; Social media; NLP; Mining; Emotions; COVID-19
Public URL https://rgu-repository.worktribe.com/output/1591898