Using a Rule-based Model to Detect Arabic Fake News Propagation during Covid-19

Abstract

Since the emergence of the Covid-19, both factual and false information about the new virus has been disseminated. Fake news harms societies and must be combated. This research aims to identify Arabic fake news tweets and classify them into six categories: entertainment, health, politics, religious, social, and sports. The study also aims to uncover patterns in the spread of Arabic fake news associated with the Covid-19 pandemic. The researchers created an Arabic dictionary and used text classification based on a rule-based system to detect and categorize fake news. A dataset consisting of 5 million tweets was analyzed. The developed model achieves an overall accuracy of 78.1% with 70% precision and 98%recall. The model detected more than 26006 fake news tweets. Interestingly we found an association between the number of fake news tweets and dates. The result demonstrates that as more information and knowledge about Covid-19 become available over time, people's awareness increase, while the number of fake news tweets decreases. The categorization of false news indicates that the social category was highest in all Arab countries except Palestine, Qatar, Yemen, and Algeria. Conversely, fake news related to the entertainment category was the weakest dissemination in most Arab countries.

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

“…x i = rand (1, d) . * (ub − lb) + lb (11) In Eq. ( 11), the hunter position or prey can be represented as X i , lb, and ub denote the minimal and maximal value for the problem variable (lower and upper boundaries), and d shows the amount of parameters (dimensions) of the problem.…”

“…In the Arab world, fake news has affected events occurring all over Arabic-speaking nations [9]. Uncertain times are even tougher; the public cannot decide what events happened and what was being reported incorrectly [10,11]. Many earlier studies have been carried out on identifying fake news utilizing DL pre-trained methods from transformers [12,13].…”

A comparison of artificial intelligence models used for fake news detection

“…These AI methods are useful for fake news classification because they are effective at identifying patterns and structures in the data. For example, a Rule-based Model used to detect Arabic Fake News propagation during Covid-19 achieved a 79,7% accuracy (Alotaibi and Alhammad 2022), which was trained on a dataset of 5015111 tweets and is quite a great success keeping in mind the limitations arising from the difficulty of processing Arabic language.…”

A Fake News Detection System based on Combination of Word Embedded Techniques and Hybrid Deep Learning Model

“…In recent years, many researchers have attempted to create a system for identifying the fake news [21]- [28]. In [29], the authors achieved an accuracy of 92%.…”