This is a preview and has not been published.

An Effective Hybrid Deep Neural Network for Arabic Fake News Detection




Arabic Fake News, Hybrid Deep Neural Network, LSTM, Text-CNN, Word Embedding.


Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural Network (Text-CNN) and Long Short-Term Memory (LSTM) architecture to produce efficient hybrid model. Text-CNN is used to identify the relevant features, whereas the LSTM is applied to deal with the long-term dependency of sequence. The results showed that when trained individually, the proposed model outperformed both the Text-CNN and the LSTM. Accuracy was used as a measure of model quality, whereby the accuracy of the Hybrid Deep Neural Network is (0.914), while the accuracy of both Text-CNN and LSTM is (0.859) and (0.878), respectively. Moreover, the results of our proposed model are better compared to previous work that used the same dataset (AraNews dataset).


Download data is not yet available.


Aljwari F, Alkaberi W, Alshutayri A , Aldhahri E, Aljojo N, Abouola O. Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News. Postmodern Openings. 2022; 13: 1-14.

Szczepański M, Pawlicki M, Kozik R, Choraś M. New explainability method for BERT-based model in fake news detection. Sci Rep. 2021; 11: 1-13. .

Thaher T, Saheb M, Turabieh H, Chantar H. Intelligent detection of false information in arabic tweets utilizing hybrid harris hawks based feature selection and machine learning models. Symmetry. 2021; 13: 1-24. 2021.

Kim J, Tabibian B, Oh A, Schölkopf B, Gomez-Rodriguez M. Leveraging the crowd to detect and reduce the spread of fake news and misinformation. In: Proceedings of the eleventh ACM international conference on web search and data mining. 2018: 324-332.

Meesad P. Thai Fake News Detection Based on Information Retrieval, Natural Language Processing and Machine Learning. SN comput sci. 2021; 2: 1-17.

Jardaneh G, Abdelhaq H, Buzz M, Johnson D. Classifying arabic tweets based on credibility using content and user features. In: IEEE JEEIT. 2019: 596-601.

Chauhan T, Palivela H. Optimization and improvement of fake news detection using deep learning approaches for societal benefit. Int J Inf Manage Data Insights. 2021; 1: 1-11.

Wani, Joshi I, Khandve S, Wagh V, Joshi R. Evaluating deep learning approaches for covid19 fake news detection. In: International Workshop on Combating On line Ho st ile Posts in Regional Languages dur ing Emerge ncy Si tuation. Springer. arXiv. 2021: 153-163.

Aldwairi M, Alwahedi A. Detecting fake news in social media networks. Procedia Comput Sci. 2018; 141: 215-222.

Nagoudi EB, Elmadany A, Abdul-Mageed M, Alhindi T, Cavusoglu H. Machine generation and detection of Arabic manipulated and fake news. In: Proceedings of the Fifth Arabic Natural Language Processing Workshop. 2020: 69-84, 2020.

Luo X. Efficient english text classification using selected machine learning techniques. Alex Eng J. 2021; 60: 3401-3409. .

Kadhim KA. Survey on supervised machine learning techniques for automatic text classification. Artif Intell Rev. 2019; 52: 273-292. .

Jang B, Kim M, Harerimana G, Kang SU, Kim JW. Bi-LSTM model to increase accuracy in text classification: Combining Word2vec CNN and attention mechanism. Appl Sci. 2020; 10: 1-14.

Li D, Deng L, Gupta BB, Wang H, Choi C. A novel CNN based security guaranteed image watermarking generation scenario for smart city applications. Inf Sci. 2019; 479: 432-447. .

Hussien Z, Dhannoon B. Anomaly Detection Approach Based on Deep Neural Network and Dropout. Baghdad Sci J. 2020; 17(2): 701-709.

Wotaifi TA, Al-Shamery ES. Modified Random Forest based Graduates Earning of Higher Education Mining. Int J Comput Inf Syst Ind Manag Appl.. 2020; 12: 56-65.

Du J, Vong CM, Chen CP .Novel efficient RNN and LSTM-like architectures: Recurrent and gated broad learning systems and their applications for text classification. IEEE Trans Cybern. 2020; 51: 1586-1597. .

Wotaifi TA, Al-Shamery ES. Fuzzy-Filter Feature Selection for Envisioning the Earnings of Higher Education Graduates. Int J Adv Comput Tecnol. 2018; 7(12): 2969-2975..

Asroni A, Ku-Mahamud KR, Damarjati C, Slamat HB. Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network. Baghdad Sci J. 2021; 18(2): 925-936.

Hamzah NA, Dhannoon BN. The Detection of Sexual Harassment and Chat Predators Using Artificial Neural Network. Karbala Int J Mod. 2021; 7(4): 301-312. .

Ibrahim V, Bakar JA, Harun NH, Abdulateef AF. A word cloud model based on hate speech in an online social media environment. Baghdad Sci J. 2021; 18(2): 937-946.

Wang S, Zhou W, Jiang C. A survey of word