A Hybrid Deep Neural Network for Arabic Fake News Classification based on Temporal CNN and BiLSTM with Attention
محورهای موضوعی : Natural Language Processing
Azhar A Hadi
1
,
Abbas F Hommadi
2
,
Hussein A Ismael
3
1 - Electrical Engineering Department, Engineering College, University of Babylon, Babylon, Iraq
2 - Electrical Engineering Department, Engineering College, University of Babylon, Babylon, Iraq
3 - Information Technology College, University of Babylon, Babylon, Iraq.
کلید واژه: Arabic Fake News, Temporal Convolution Network, BiLSTM, Attention Mechanism, Deep Learning,
چکیده مقاله :
The proliferation of fake news on social media poses a significant threat to societal harmony, especially in languages like Arabic, which is distinguished by its complexity. Thus, Artificial intelligence techniques are necessary to mitigate the impact of misinformation. Many researchers have conducted this challenge by introducing machine learning and deep learning approaches. This study presents a new hybrid deep learning network for enhancing the classification of Arabic fake news. This network combines three mechanisms: Temporal Convolutional Networks, Bidirectional Long Short-Term Memory, and Attention Mechanism. This mixture model produces a strong feature extraction process with temporal awareness. Moreover, the attention layer enables the model to focus only on relevant features and ignore irrelevant ones to concentrate on salient features. Also, several stages of pre-processing Arabic text, representing words using a pre-trained word embedding model (Glove, Ara2Vec), and extracting features through advanced layers (BiLSTM, TCN, and Attention). Extensive experimentation on large-scale and well-known Arabic fake news datasets (AFND and AraNews) demonstrates the efficacy of the proposed model. The hybrid model shows superior performance compared to baseline and previous state-of-the-art studies by achieving an accuracy of 88% and 95% on the AFND and AraNews datasets, respectively. Our results show that the suggested model can be used as a powerful technique to restrain the widespread online misinformation in the Arabic language.
The proliferation of fake news on social media poses a significant threat to societal harmony, especially in languages like Arabic, which is distinguished by its complexity. Thus, Artificial intelligence techniques are necessary to mitigate the impact of misinformation. Many researchers have conducted this challenge by introducing machine learning and deep learning approaches. This study presents a new hybrid deep learning network for enhancing the classification of Arabic fake news. This network combines three mechanisms: Temporal Convolutional Networks, Bidirectional Long Short-Term Memory, and Attention Mechanism. This mixture model produces a strong feature extraction process with temporal awareness. Moreover, the attention layer enables the model to focus only on relevant features and ignore irrelevant ones to concentrate on salient features. Also, several stages of pre-processing Arabic text, representing words using a pre-trained word embedding model (Glove, Ara2Vec), and extracting features through advanced layers (BiLSTM, TCN, and Attention). Extensive experimentation on large-scale and well-known Arabic fake news datasets (AFND and AraNews) demonstrates the efficacy of the proposed model. The hybrid model shows superior performance compared to baseline and previous state-of-the-art studies by achieving an accuracy of 88% and 95% on the AFND and AraNews datasets, respectively. Our results show that the suggested model can be used as a powerful technique to restrain the widespread online misinformation in the Arabic language.
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