Fake or Real? A Study of Arabic Satirical Fake News

Saadany, Hadeel; Mohamed, Emad A.; Orăsan, Constantin

doi:10.48550/arxiv.2011.00452

Cited by 5 publications

(5 citation statements)

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“…The J48 DT classifier yielded the highest predictive accuracy, 89.9%. In [9], the author proposes multiple machine learning models to classify satirical fake Arabic news by exploiting linguistic features inherent in Arabic satirical fake news. In addition, the author introduces a novel dataset comprised of 3,185 fake news articles and 3,710 real news articles.…”

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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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: 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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“…Once the R5 value is small when compared to β, the search agent is assumed as a hunter, and the following location of the searching agent is upgraded; once the R5 value is large when compared to β, the search agent regards prey, and the following location of the searching agent is upgraded. In this study, the Arabic fake newidentification outcomes of the HPOHDL-FND model are tested using the Covid19Fakes dataset [26] and Satirical dataset [27], as depicted in Table 1. Fig.…”

Section: Hyperparameter Tuning Modulementioning

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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“…Another approach exploited textual features defined in [15] to automatically identified satire news. They released a dataset that was collected from a variety of news websites.…”

Section: Related Workmentioning

confidence: 99%

The Detection of Fake News in Arabic Tweets Using Deep Learning

Alyoubi,

Kalkatawi,

Abukhodair

2023

Applied Sciences

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Fake news has been around for a long time, but the rise of social networking applications over recent years has rapidly increased the growth of fake news among individuals. The absence of adequate procedures to combat fake news has aggravated the problem. Consequently, fake news negatively impacts various aspects of life (economical, social, and political). Many individuals rely on Twitter as a news source, especially in the Arab region. Mostly, individuals are reading and sharing regardless of the truth behind the news. Identifying fake news manually on these open platforms would be challenging as they allow anyone to build networks and publish the news in real time. Therefore, creating an automatic system for recognizing news credibility on social networks relying on artificial intelligence techniques, including machine learning and deep learning, has attracted the attention of researchers. Using deep learning methods has shown promising results in recognizing fake news written in English. Limited work has been conducted in the area of news credibility recognition for the Arabic language. This work proposes a deep learning-based model to detect fake news on Twitter. The proposed model utilizes the news content and social context of the user who participated in the news dissemination. In seeking an effective detection model for fake news, we performed extensive experiments using two deep learning algorithms with varying word embedding models. The experiments were evaluated using a self-created dataset. The experimental results revealed that the MARBERT with the convolutional neural network (CNN) model scores a superior performance in terms of accuracy and an F1-score of 0.956. This finding proves that the proposed model accurately detects fake news in Arabic Tweets relating to various topics.

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Fake or Real? A Study of Arabic Satirical Fake News

Cited by 5 publications

References 0 publications

Detecting Arabic Misinformation Using an Attention Mechanism-Based Model

Detecting Arabic Misinformation Using an Attention Mechanism-Based Model

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

The Detection of Fake News in Arabic Tweets Using Deep Learning

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