An overview of Fake News DetectionusingBidirectional Long Short-TermMemory(BiLSTM)Models
Abstract
This study aims to provide an overview of the existing research on fake news detection using Bidirectional Long Short-Term Memory (Bi LSTM) models. The paper focuses on the advantages of using Bi LSTM over other machine learning techniques, various feature extraction methods, and the challenges faced in fake news detection. By reviewing the state-of-the-art studies, this survey highlights the performance and effectiveness of Bi LSTM in addressing the fake news detection problem.
Keywords:
BiLSTM, Fake news detection, recurrent neural network, Natural language processing
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All published work in this journal is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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