Document Type : Original Article
Authors
1
Computer Science Department, Helwan University, Faculty of Computers and Artificial Intelligence, Cairo, Egypt Computer Science Department, Luxor University, Faculty of Computers and Information, Luxor, Egypt
2
Computer Science Department, Faculty of Computers and Artificial Intelligence Helwan University, Cairo, Egypt
3
Computer Science, Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt.
Abstract
In the current era, social media serves as a highly influential platform for news dissemination, where information is readily accessible to the public without verification of its authenticity. Consequently, it has become an effective medium for the propagation of false information, enabling individuals to share unverified content that reaches a vast audience. This capability facilitates the creation and spread of misinformation, potentially misleading society for personal or corporate benefit. Fake news is often utilized to influence government policies and tarnish the reputations of individuals. In the medical field, The COVID-19 pandemic has resulted in a substantial rise in the use of social media platforms as sources of information. Unfortunately, this surge in usage has also contributed to the widespread dissemination of misinformation concerning healthcare and the pandemic. So, to facilitate the consequences of fake news on society, several research projects have been proposed to identify fake news with the best accuracy to protect its outcome. For this, we present a comparative literature of the fake text classification architectures in this paper and an overall analysis of the state of the art techniques and challenges that have been conducted on fake information and propaganda in Arabic and English texts, as well as the different approaches that have been taken to address this problem.
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