Ensemble Based Low Complexity Arabic Fake News Detection

Authors

  • Mohammed E. Almandouh Information system department, Faculty of Management Technology and information system, Port said University, Port Said, Egypt. https://orcid.org/0009-0004-7369-0855
  • Mohammed F. Alrahmawy Head of Computer Science Department, Faculty of Computer and Information, Mansoura University, Mansoura, Egypt https://orcid.org/0000-0001-8978-8268
  • Mohamed Eisa Information technology Department, Faculty of Management Technology and Information Systems, Port said University, Port Said, Egypt. https://orcid.org/0000-0003-2685-0057
  • A. S. Tolba Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt. New Heliopolis Institute for Engineering & Automotive and Energy Technologies, New Heliopolis, Egypt https://orcid.org/0000-0002-2751-367X

Keywords:

Machine learning, Deep learning, Natural Language Processing, Fake News, Arabic Fake Detection (AFD), Ensemble Learning (EL)

Abstract

Nowadays, Due to the growth of online communication, many people use social media platforms and produce a lot of content. Fake news creates negative perceptions of society. The rise of social media networks has given fake news a platform to rapidly gain popularity among users. Identifying and labelling Arabic fake news represents a big challenge because of the large amount of heterogeneous content in addition to the limited related Arabic datasets available. machine learning (ML), Natural language processing (NLP) and deep learning (DL) are commonly used to increase the speed and automate the analytical process of this huge amount of content and to transform unstructured text into structured form. In this study, a corpus of news websites is developed to determine fake news using some machine learning techniques; this includes a dataset 3185 fake news articles and a new dataset consists of 1453 real news articles. This paper shows that, using an aggregation of machine learning and ensemble methods, we can make a prediction model for fake news that has an accuracy of up to 100%. with low complexity, which can save power and energy, and we can use it as a reference for the detection of fake news in Egypt and Arab countries.

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Workflow of machine generation of manipulated text (Arabic fake news detection)

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Published

17.02.2023

How to Cite

Almandouh , M. E. ., Alrahmawy, M. F. ., Eisa, M. ., & Tolba, A. S. . (2023). Ensemble Based Low Complexity Arabic Fake News Detection. International Journal of Intelligent Systems and Applications in Engineering, 11(2), 1022 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2985

Issue

Vol. 11 No. 2 (2023)

Section

Research Article

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Copyright (c) 2023 Mohammed E. Almandouh , Mohammed F. Alrahmawy, Mohamed Eisa, A. S. Tolba

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