Abstract
The primary objective of this paper is to develop a method by which a Deep Neural Network can be used to perform Fake News detection without implementing any complex Natural Language Processing techniques. A key element of the proposed structure consists of a data processing component that manipulates the news article data (shape, data type, etc.) in the database so that it can be used in-side a Deep Neural Network structure, without the need for additional complex NLP techniques. A Deep Neural Network model that can perform binary classification on a given set of news articles was built. The method was implemented in Python using the Jupyter Notebook framework. The algorithm has an accuracy of at least 70% on the testing data set, leading to conclusive results regarding the be-longing of the considered material to the fake news categories.
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Avram, C., Mesaroş, G., Aştilean, A. (2022). Deep Neural Networks in Fake News Detection. In: Machado, J., et al. Innovations in Mechatronics Engineering II. icieng 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-09385-2_3
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