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

Alshahrani, Hala J.; Hassan, Abdulkhaleq Q. A.; Tarmissi, Khaled; Othman, Mahmoud; Motwakel, Abdelwahed; Yaseen, Ishfaq; Abdelmageed, Amgad Atta; Eldesouki, Mohamed I.

doi:10.32604/cmc.2023.034821

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…The paper Alshahrani et al ( 2023 ) addresses the issue of the spread of rumors or false information on social media platforms among Arab nations. The study develops a new hunter-prey optimization with a hybrid deep learning-based fake news detection (HPOHDL-FND) model on the Arabic corpus.…”

Section: Current Research Analysismentioning

confidence: 99%

A novel approach to fake news classification using LSTM-based deep learning models

Padalko,

Chomko,

Chumachenko

2024

Front. Big Data

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The rapid dissemination of information has been accompanied by the proliferation of fake news, posing significant challenges in discerning authentic news from fabricated narratives. This study addresses the urgent need for effective fake news detection mechanisms. The spread of fake news on digital platforms has necessitated the development of sophisticated tools for accurate detection and classification. Deep learning models, particularly Bi-LSTM and attention-based Bi-LSTM architectures, have shown promise in tackling this issue. This research utilized Bi-LSTM and attention-based Bi-LSTM models, integrating an attention mechanism to assess the significance of different parts of the input data. The models were trained on an 80% subset of the data and tested on the remaining 20%, employing comprehensive evaluation metrics including Recall, Precision, F1-Score, Accuracy, and Loss. Comparative analysis with existing models revealed the superior efficacy of the proposed architectures. The attention-based Bi-LSTM model demonstrated remarkable proficiency, outperforming other models in terms of accuracy (97.66%) and other key metrics. The study highlighted the potential of integrating advanced deep learning techniques in fake news detection. The proposed models set new standards in the field, offering effective tools for combating misinformation. Limitations such as data dependency, potential for overfitting, and language and context specificity were acknowledged. The research underscores the importance of leveraging cutting-edge deep learning methodologies, particularly attention mechanisms, in fake news identification. The innovative models presented pave the way for more robust solutions to counter misinformation, thereby preserving the veracity of digital information. Future research should focus on enhancing data diversity, model efficiency, and applicability across various languages and contexts.

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Section: Current Research Analysismentioning

confidence: 99%

A novel approach to fake news classification using LSTM-based deep learning models

Padalko,

Chomko,

Chumachenko

2024

Front. Big Data

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Mountain Gazelle Optimizer with Deep Learning Driven Satirical News Classification on Low-resource Language Corpus

Al-Onazi,

Alotaibi,

Hassan

et al. 2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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The development of satirical and fake news on digital platforms has source of major concern about the spread of misinformation and its control on society. As part of the Arabic language, fake news detection (FND) shows particular problems because of language difficulties and the scarcity of labeled data. FND on Arabic corpus utilizing deep learning (DL) contains leveraging advanced neural network (NN) techniques and methods to automatically recognize and classify deceptive data in the Arabic language text. This procedure is vital in combating the spread of disinformation and misinformation, promoting media literacy, and make sure the credibility of data sources for the Arabic-speaking community. Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) are common selections for FND because of their capability for learning hierarchical features and model sequential data from the text. In this view, this study develops a Mountain Gazelle Optimizer with Deep Learning-Driven Fake News Classification on Arabic Corpus (MGODL-FNCAC) technique. The presented MGODL-FNCAC approach aims to increase the performance of the fake news classification on the Arabic corpus. Primarily, the MGODL-FNCAC technique involves different stages of pre-processing to make the input data compatible for classification. For fake news detection, the MGODL-FNCAC technique applies the deep belief network (DBN) model. At last, the MGO approach can be used for the better hyperparameter tuning of the DBN approach, which supports in enhancing the overall training process and detection rate. The simulation outcomes of the MGODL-FNCAC technique can be examined on Arabic corpus data. The extensive outcomes exhibit the importance of the MGODL-FNCAC system over other methodologies with maximum accuracy of 97.68% and 95.14% on Covid19Fakes and Satirical dataset, respectively.

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Hunter–Prey Optimization Algorithm: a review

Lahari,

Janamala

2024

Journal of Electrical Systems and Inf Technol

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The Hunter–Prey Optimization Algorithm (HPO) is a nature-inspired optimization technique influenced by the predator–prey relationships observed in nature. Over the years, HPO has gained attention as a promising method for solving complex optimization problems. This review article provides a comprehensive analysis and a bibliographic study of the Hunter–Prey Optimization Algorithm. It explores its origins, underlying principles, applications, strengths, weaknesses, and recent developments in detail. By delving into various facets of HPO, this review aims to shed light on its effectiveness and potential, inspiring the researchers to address real-world optimization challenges.

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

Cited by 4 publications

References 27 publications

A novel approach to fake news classification using LSTM-based deep learning models

A novel approach to fake news classification using LSTM-based deep learning models

Mountain Gazelle Optimizer with Deep Learning Driven Satirical News Classification on Low-resource Language Corpus

Hunter–Prey Optimization Algorithm: a review

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