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An Attribute-wise Attention model with BiLSTM for an efficient Fake News Detection

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Nowadays, fake news propaganda is the main threat to society, as it has the potential to misdirect public behaviour and provoke violence and extremism. Recently, to prevent the spread of misinformation, researchers have devised various fake news detection models with deep learning techniques. This research aims to develop a unique deep architecture to capture the basic insights about the document in the form of a summarised feature vector representation for better fake news detection. The proposed model, namely AA-BiLSTM includes Bi-directional Long Short-Term Memory (Bi-LSTM) and an attribute-wise attention mechanism based on Convolutional Neural Network (CNN) as the CNN models extract higher-level features using convolutional layers and average pooling layers. Moreover, this model suitably pays attention to the word or sentence based on the dependent attribute by applying the attention mechanism after the fusing process. The performance of the proposed AA-BiLSTM has been experimentally evaluated on four benchmark datasets, namely Kaggle fake_real_news [2022], Kaggle fake_real_news [2016], ISOT and Liar datasets, and compared with the existing state-of-the-art fake news detection methods. For the Kaggle fake_real_news 2016 and ISOT fake news datasets, the AA-BiLSTM algorithm achieved an accuracy of more than 99%. In the Liar dataset, the AA-BiLSTM method surpassed all basic models with an accuracy of 60.31%.

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Data Availability

The datasets analyzed during the current study are available in public repositories.






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The authors are very grateful to the family members and research committee for their constant support and to the editors and reviewers for their valuable and prolific suggestions to refine the quality of the paper.

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Correspondence to M. Gethsiyal Augasta.

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Merryton, A.R., Gethsiyal Augasta, M. An Attribute-wise Attention model with BiLSTM for an efficient Fake News Detection. Multimed Tools Appl 83, 38109–38126 (2024).

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