Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News

Aljwari, Fatima; Alkaberi, Wahaj; Alshutayri, Areej; Aldhahri, Eman; Aljojo, Nahla; Abouola, Omar

doi:10.18662/po/13.1sup1/411

Cited by 6 publications

(8 citation statements)

References 24 publications

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“…Although the BiLSTM model achieved good results, with precision, recall, and F1-score of 92%, 93%, and 93%, respectively, its performance was lower than that of the Att-BiLSTM model, thus proving the efficacy of the attention mechanism in enhancing the overall model performance. To evaluate the performance of our model, we compared the performance of our proposed model with the baseline models in studies [10,11,12,14] which utilized the same dataset. We compared our model with baseline models in four studies from the related works section that utilized the AraNews dataset and the ArCOV19-Rumors dataset, including:…”

Section: Resultsmentioning

confidence: 99%

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Detecting Arabic Misinformation Using an Attention Mechanism-Based Model

Nema,

Al-Tai

2024

Iraqi Journal for Computer Science and Mathematics

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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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Section: Resultsmentioning

confidence: 99%

“…In [12], the author employs the AraNews dataset as a foundation for model development. The methodology included the implementation of the Term Frequency-Inverse Document Frequency (TF-IDF) technique, a strategic approach used for feature extraction in the form of word vectors.…”

Section: Related Workmentioning

confidence: 99%

Detecting Arabic Misinformation Using an Attention Mechanism-Based Model

Nema,

Al-Tai

2024

Iraqi Journal for Computer Science and Mathematics

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“…In order to distinguish true news and fake news and for deep learning techniques to be used, it is necessary to have a sufficient dataset. The dataset that has been used in this study is called AraNews dataset and is available at kaggle.com 1 . The AraNews dataset is a large and generally Arabic fake news dataset that was collected from many newspapers on many topics.…”

Section: Datasetmentioning

confidence: 99%

“…This Information or claims that have been verified as incorrect are called fake news. This phenomenon is a serious problem because its spread is rapid and thus threatens societal peace 1 . In recent years, interest in addressing fake news (misinformation) and reducing its problems has attracted the attention of many researchers by employing artificial intelligence techniques 2 .…”

Section: Introductionmentioning

confidence: 99%

An Effective Hybrid Deep Neural Network for Arabic Fake News Detection

Wotaifi

Dhannoon²

2023

Baghdad Sci.J

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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).

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“…Then, for extracting text features from the articles, the author frames a novel technique of constituting Arabic lexical wordlists and devises an Arabic NLP tool for performing text feature extractions. Aljwari et al [17] intend to sort out how it forecasts the features which act as a reason for the spreading of Arabic online fake news. It utilizes Random Forest (RF), Naive Bayes (NB), and Logistic Regression (LR) to accomplish this.…”

Section: Related Workmentioning

confidence: 99%

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

Alshahrani¹,

Hassan²,

Tarmissi³

et al. 2023

Computers, Materials &Amp; Continua

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Nowadays, the usage of social media platforms is rapidly increasing, and rumours or false information are also rising, especially among Arab nations. This false information is harmful to society and individuals. Blocking and detecting the spread of fake news in Arabic becomes critical. Several artificial intelligence (AI) methods, including contemporary transformer techniques, BERT, were used to detect fake news. Thus, fake news in Arabic is identified by utilizing AI approaches. This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection (HPOHDL-FND) model on the Arabic corpus. The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format. Besides, the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network (LSTM-RNN) model for fake news detection and classification. Finally, hunter prey optimization (HPO) algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model. The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets. The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57% and 93.53% on Covid19Fakes and satirical datasets, respectively.

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Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News

Cited by 6 publications

References 24 publications

Detecting Arabic Misinformation Using an Attention Mechanism-Based Model

Detecting Arabic Misinformation Using an Attention Mechanism-Based Model

An Effective Hybrid Deep Neural Network for Arabic Fake News Detection

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

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