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Toxic Fake News Detection and Classification for Combating COVID-19 Misinformation by Mudasir Ahmad Wani; Mohammad ELAffendi; Kashish Ara Shakil; Ibrahem Mohammed Abuhaimed; Anand Nayyar; Amir Hussain; Ahmed A. Abd El-Latif is a scholarly article available to read on EtoBox.

What is Toxic Fake News Detection and Classification for Combating COVID-19 Misinformation about?

The emergence of COVID-19 has led to a surge in fake news on social media, with toxic fake news having adverse effects on individuals, society, and governments. Detecting toxic fake news is crucial, but little prior research has been done in this area. This study aims to address this gap and identify toxic fake news to save time spent on examining nontoxic fake news. To achieve this, multiple datasets were collected from different online social networking platforms such as Facebook and Twitter. The latest samples were obtained by collecting data based on the topmost keywords extracted from the existing datasets. The instances were then labeled as toxic/nontoxic using toxicity analysis, and traditional machine-learning (ML) techniques such as linear support vector machine (SVM), conventional random forest (RF), and transformer-based ML techniques such as bidirectional encoder representations from transformers (BERT) were employed to design a toxic-fake news detection (FND) and classification system. As per the experiments, the linear SVM method outperformed BERT SVM, RF, and BERT RF with an accuracy of 92% and F 1 -score, F 2 -score, and F 0.5 -score of 95%, 85%, and 87%, respective

Author

Mudasir Ahmad Wani; Mohammad ELAffendi; Kashish Ara Shakil; Ibrahem Mohammed Abuhaimed; Anand Nayyar; Amir Hussain; Ahmed A. Abd El-Latif

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2024

Language

EN