Deep Learning for Fake News Detection: Literature Review
DOI:
https://doi.org/10.23851/mjs.v34i2.1292Keywords:
Deep learning, fake news detection, natural language processing, machine learning, text classification, information credibility, social media analysis, semantic analysis, CNN, RNN, multimodal, datasets, word embedding, LSTM, Hybrid, BERTAbstract
The use of Deep Learning (DL) for identifying false or misleading information, known as fake news, is a growing area of research. Deep learning, a form of machine learning that utilizes algorithms to learn from large data sets, has shown promise in detecting fake news. The spread of fake news can cause significant harm to society economically, politically, and socially, and it has become increasingly important to find ways to detect and stop its spread. This paper examines current studies that use deep learning methods, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), as well as the multi-model approach, to detect fake news. It also looks at the use of word embedding models to convert text to vector representations and the datasets used for training models. Furthermore, the paper discusses the use of the attention mechanism in conjunction with deep learning to process sequential data.
Received: 26/01/2023
Revised: 06/03/2023
Accepted: 05/04/2023
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