JointBert for Detecting Arabic Fake News

Shishah, Wesam

doi:10.1109/access.2022.3185083

Cited by 12 publications

(7 citation statements)

References 21 publications

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“…For deep learning, the BiLSTM model provided the highest accuracy across all three datasets. Meanwhile, Shishah [88] performed another study, proposing a model called JointBERT for detecting the Arabic language. JointBERT in this research used Named Entity Recognition (NER) and Relative Features Classification (RFC) as parameters.…”

Section: Fake Nwes Detection and Arabic Languagementioning

confidence: 99%

A Review of Fake News Detection Techniques for Arabic Language

Alotaibi,

Al-Dossari

2024

IJACSA

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The growing proliferation of social networks provides users worldwide access to vast amounts of information. However, although social media users have benefitted significantly from the rise of various platforms in terms of interacting with others, e.g., expressing their opinions, finding products and services, and checking reviews, it has also raised critical problems, such as the spread of fake news. Spreading fake news not only affects individual citizens but also governments and countries. This situation necessitates the immediate integration of artificial intelligence methodologies to address and alleviate this issue effectively. Researchers in the field have leveraged different techniques to mitigate this problem. However, research in the Arabic language for fake news detection is still in its early stages compared with other languages, such as English. This review paper intends to provide a clear view of Arabic research in the field. In addition, the paper aims to provide other researchers working on solving Arabic fake news detection problems with a better understanding of the common features used in extraction, machine learning, and deep learning algorithms. Moreover, a list of publicly available datasets is provided to give an idea of their characteristics and facilitate researcher access. Furthermore, some of limitations and challenges related to Arabic fake news and rumor detection are discussed to encourage other researchers.

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Section: Fake Nwes Detection and Arabic Languagementioning

confidence: 99%

A Review of Fake News Detection Techniques for Arabic Language

Alotaibi,

Al-Dossari

2024

IJACSA

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“…Therefore, there is a growing interest in methods that combine multimodal information for fake news detection. Shishah et al [6] used deep neural networks to incorporate multimodal information into fake news detection. They proposed an attention-based recurrent neural network that integrates textual, visual, and social context information.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, real news tends to be more objective and rigorous, with higher-quality accompanying visuals. Current multimodal approaches [6][7][8] commonly employ recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to capture the characteristics of fake news in terms of both textual and visual modalities at the surface level. However, the surfacelevel characteristics of fake news are highly datasetdependent, which makes methods that perform well on specifc datasets often struggle to generalize effectively to new datasets and are prone to misjudging fake news with less obvious surface-level characteristics.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Semantic Model for Multimodal Fake News Detection

2024

IJIES

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Social media has gradually become the primary news source for people in recent years, providing convenience but also leading to the spread of false information. With the rise of media-rich social media platforms, fake news has evolved from single-text to multimodal formats, prompting increased attention to multi-modal fake news detection. However, most existing methods rely on representation-level features that are closely tied to the dataset, resulting in insufficient modelling of semantic-level features and a limited ability to generalize to new data. To address this issue, we propose a semantically enhanced multimodal fake news detection method that utilizes pre-trained language models to capture implicit factual knowledge and explicitly extracts visual entities to better understand the deep semantics of multimodal news. We also extract visible features at different semantic levels, use a text-guided attention mechanism to model semantic interactions between text and images, and integrate multimodal features. Experimental results on real datasets based on Weibo news demonstrate that the proposed method outperforms other methods with an accuracy of 0.895 in multimodal fake news detection.

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“…The detection model faces severe complications due to a lack of tagged data identifying fake news [6]. Fake news can be P. Narang, A. V. Singh and H. Monga 2 broadly categorized as clickbait, misinformation, disinformation, hoaxes, satire, parody, deceptive news, rumours, and similar data [7] [8]. Fake news on social media is circulated several times, leading to substantial detrimental effects on society due to misleading information [9] [10].…”

Section: Introductionmentioning

confidence: 99%

Integrating Metaheuristics and Two-Tiered Classification for Enhanced Fake News Detection with Feature Optimization

Narang,

Singh,

Monga

2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: The challenge of distributing false information continues despite the significant impact of social media on opinions. The suggested framework, which is a metaheuristic method, is presented in this research to detect bogus news. Employing a hybrid metaheuristic RDAVA methodology coupled with Bi-LSTM, the method leverages African Vulture Optimizer and Red Deer Optimizer.OBJECTIVES: The objective of this study is to assess the effectiveness of the suggested model in identifying false material on social media by employing social network analysis tools to combat disinformation.METHODS: Employing the data sets from BuzzFeed, FakeNewsNet, and ISOT, the suggested model is implemented on the MATLAB Platform and acquires high accuracy rates of 97% on FakeNewsNet and 98% on BuzzFeed and ISOT. A comparative study with current models demonstrates its superiority.RESULTS: Outperforming previous models with 98% and 97% accuracy on BuzzFeed/ISOT and FakeNewsNet, respectively, the suggested model shows remarkable performance.CONCLUSION: The proposed strategy shows promise in addressing the problem of false information on social media in the modern day by effectively countering fake news. Its incorporation of social network analysis methods and metaheuristic methodologies makes it a powerful instrument for identifying false news.

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JointBert for Detecting Arabic Fake News

Cited by 12 publications

References 21 publications

A Review of Fake News Detection Techniques for Arabic Language

A Review of Fake News Detection Techniques for Arabic Language

Deep Learning Based Semantic Model for Multimodal Fake News Detection

Integrating Metaheuristics and Two-Tiered Classification for Enhanced Fake News Detection with Feature Optimization

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