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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    Titel :

    Deep Neural Networks in Fake News Detection


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Machado, José (Herausgeber:in) / Soares, Filomena (Herausgeber:in) / Trojanowska, Justyna (Herausgeber:in) / Yildirim, Sahin (Herausgeber:in) / Vojtěšek, Jiří (Herausgeber:in) / Rea, Pierluigi (Herausgeber:in) / Gramescu, Bogdan (Herausgeber:in) / Hrybiuk, Olena O. (Herausgeber:in) / Avram, Camelia (Autor:in) / Mesaroş, George (Autor:in)

    Kongress:

    International Conference Innovation in Engineering ; 2022 ; Minho, Portugal June 28, 2022 - June 30, 2022



    Erscheinungsdatum :

    2022-06-21


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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