International Journal of Advances in Applied Computational Intelligence
Journal DOI
2833-5600ISSN (Online)
Volume 2 , Issue 1 , PP: 29-35, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Waleed Abd Elkhalik 1 *
Doi: https://doi.org/10.54216/IJAACI.020104
In the era of information overload and the widespread dissemination of news through various online platforms, the identification and mitigation of fake news have become imperative. This paper presents a comprehensive investigation into the application of Transformer Networks for accurate fake news classification. Transformers, known for their ability to model long-range dependencies and capture contextual information effectively, have demonstrated outstanding performance in natural language processing tasks. Leveraging this strength, we propose a simple but effective approach that employs Transformer-based architectures to discern fake news from genuine information with high precision. In our approach, we explore various techniques, such as attention mechanisms, positional encoding, and self-attention layers, to capture important contextual relationships and optimize the classification process. Through extensive experimentation, we demonstrate the effectiveness of our approach in accurately identifying and classifying fake news articles. Our proposed model achieves state-of-the-art performance on a public benchmark dataset, surpassing existing approaches.
Applied Machine Learning , Computation intelligence , Transformer Networks , Fake News detection.
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