Arabic fake news detection based on deep contextualized embedding models

Nassif, Ali Bou; Elnagar, Ashraf; Elgendy, Omar; Afadar, Yaman

doi:10.1007/s00521-022-07206-4

Cited by 45 publications

(14 citation statements)

References 29 publications

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“…Therefore, in this work, we have tried to figure out the performance of several models and their parameter tuning for the prediction of news containing false information about COVID-19. The proposed work is different from the study [22] discussed above in the sense that it evaluates eight DL classifiers for detecting fake news in the Arabic language. Whereas, our work evaluates six ML and two DL classifiers for the same task in the English language.…”

Section: Related Workmentioning

confidence: 89%

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Evaluating Intelligent Methods for Detecting COVID-19 Fake News on Social Media Platforms

Alhakami

Baz

et al. 2022

Electronics

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The advent of Internet-based technology has made daily life much easy than earlier days. The exponential rise in the popularity of social media platforms has not only connected people from faraway places, but has also increased communication among humans. However, in several instances, social media platforms have also been utilized for unethical and criminal activities. The propagation of fake news on social media during the ongoing COVID-19 pandemic has deteriorated the mental and physical health of people. Therefore, to control the flow of fake news regarding the novel coronavirus, several studies have been undertaken to automatically detect the fake news about COVID-19 using various intelligent techniques. However, different studies have shown different results on the performance of the predicting models. In this paper, we have evaluated several machine learning and deep learning models for the automatic detection of fake news regarding COVID-19. The experiments were carried out on two publicly available datasets, and the results were assessed using several evaluation metrics. The traditional machine learning models produced better results than the deep learning models in predicting fake news.

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Section: Related Workmentioning

confidence: 89%

“…The authors developed transformer-based classifiers for the task. They constructed a dataset of fake news in the Arabic language for evaluating their proposed models, and achieved an accuracy score of more than 98 percent [22].…”

Section: Related Workmentioning

confidence: 99%

Evaluating Intelligent Methods for Detecting COVID-19 Fake News on Social Media Platforms

Alhakami

Baz

et al. 2022

Electronics

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“…According to a study [33], False-Negative (FN) and False-Positive (FP) are important for rumor detection tasks. Misclassifying rumors as non-rumors, and vice versa, affects and misleads society by spreading untruthful news.…”

Section: Discussionmentioning

confidence: 99%

“…The study concluded that transformer-based models outperformed neural-based ones. While the study [33] detected fake news using eight BERT transformer-based models, two were multilingual, and the remaining were BERT models for the Arabic language.…”

Section: A Rumor Detection Using Unimodal Approachesmentioning

confidence: 99%

Multimodal Arabic Rumors Detection

et al. 2023

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Recently, the use of social media platforms has increased with ease of use and fast accessibility, making such platforms a place of rumor proliferation owing to the lack of posting constraints and content authentication. Therefore, there is a need to leverage artificial intelligence techniques to detect rumors on social media platforms to prevent their adverse effects on society and individuals. Most existing works that detect rumors in Arabic target the textual features of the tweet content. Nevertheless, tweets contain different types of content, such as (text, images, videos, and URLs), and the visual features of tweets play an essential role in rumor diffusion. This study proposes an Arabic rumor detection model to detect rumors on Twitter using textual and visual image features through two types of multimodal fusion: early and late fusion. In addition, we leveraged the transfer learning of the pre-trained language and vision models. Different experiments were conducted to select the best textual and visual feature extractors for building a multimodal model. MARBERTv2 was used as a textual feature extractor, whereas the ensemble of VGG-19 and ResNet50 was used as a visual feature extractor to build the multimodal model. Subsequently, the language and vision models of the single models were used as a baseline to compare their results with those of multimodal models. Finally, the experimental results demonstrate the effectiveness of textual features in rumor detection tasks compared to multimodal models.

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“…Fake news detection (FND) can be described as "the forecasting the probabilities of a specific news article (expose, news reports, editorials, and so on) being intentionally deceptive". Other terminologies regard tasks closely based on FND, adding credibility assessment, rumour detection, misleading data detection, stance classification of news articles, rumor veracity classification, checking "the valuation of news authenticity", and claiming confirmation [5,6]. Recently, FND tasks have grasped substantial attention from the natural language processing (NLP) research community.…”

Section: Introductionmentioning

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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Arabic fake news detection based on deep contextualized embedding models

Cited by 45 publications

References 29 publications

Evaluating Intelligent Methods for Detecting COVID-19 Fake News on Social Media Platforms

Evaluating Intelligent Methods for Detecting COVID-19 Fake News on Social Media Platforms

Multimodal Arabic Rumors Detection

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

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