Detecting Arabic Misinformation Using an Attention Mechanism-Based Model




Misinformation, Attention mechanism, BiLSTM, LSTM, Bert, AraBert, contextual embeddings


The proliferation of fake news or misinformation, commonly referred to as fake news, has a significant effect on a global scale, as it is aimed at influencing public opinion as well as crowd decision-making. It is therefore crucial to verify the truthfulness of news before it is released to the public. Today, most studies on early detection of Arabic misinformation rely on machine learning methods and transformer-based models. Therefore, in the current study, we used deep learning techniques to propose a model for detecting Arabic misinformation by leveraging the contextual features of news article content. The proposed model was built based on BiLSTM and the attention mechanism. To extract features from Arabic text, we utilized a pre-trained AraBERT model, which generates contextual embeddings from text, then are fed to the BiLSTM layer as input features. Moreover, we investigated the effectiveness of the attention mechanism in improving the overall performance of the model by configuring model architecture to exclude the attention mechanism and comparing the results. Two datasets were utilized to train and evaluate the proposed model, namely, the AraNews and ArCovid19-Rumors datasets. Experimental results showed that the proposed model outperformed other existing models, achieving an accuracy of 0.96 on the ArCovid19-Rumors dataset and 0.90 on the AraNews dataset. This remarkable performance was due to the capability of the attention mechanism to enhance the overall performance, allowing the model to capture the relationship between textual features.


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Author Biography

Bashar AlEsawi, Department of Computer Science, Mustansiriyah University, Baghdad, IRAQ

Prof. Bashar Al-Esawi, a respected computer scientist, has 27 years of experience. He earned a B.Sc. in Computer Science from AL-Mansoor University College in 1996. M.Sc. in Artificial Intelligence from the University of Technology, Iraq, in 1999, and a Ph.D. in Information Security from the same university in 2004.  Since 2005, he has been actively involved in research and academia in Jordan and Iraq. He held leadership positions, including Dean of the College of Science at Mustansiriyah University (2018-2020) and Quality Assurance Manager (2012-2018). Prof. Al-Esawi's research focuses on information security, AI, machine learning, and natural language processing. He has published papers and presented at international conferences. He is also a highly regarded educator, teaching computer science courses at the undergraduate and graduate levels.




How to Cite

B. AlEsawi and M. . Haqi Al-Tai, “Detecting Arabic Misinformation Using an Attention Mechanism-Based Model”, Iraqi Journal For Computer Science and Mathematics, vol. 5, no. 1, pp. 285–298, Feb. 2024.
DOI: 10.52866/ijcsm.2024.05.01.020
Published: 2024-02-11