Work

Theoretical and applied foundations for improving fake news detection systems based on the use of CNN neural networks

Year: 2025

Type: conference-paper

Abstract: The article addresses the problem of developing an information system for fake news detection, presents the design of its architecture and describes its software implementation.It is substantiated that improving the effectiveness of fake news detection can be achieved by enhancing the method based on a multilayer convolutional neural network (CNN).The proposed enhancement involves the addition of a dropout layer, increasing the kernel size and modifying the activation function.The use of the PolitiFact and LIAR datasets for neural network training is justified.An information system implementing the proposed method has been developed.The effectiveness of the proposed method has been evaluated.The use of the TensorFlow classification model and Logistic Regression as baseline models for comparison is justified.The results demonstrate that the proposed method is generally more effective than the existing methods considered in this research.

DOI: 10.31110/colins/2025-3/008

Authors: Mikolaj Karpinski, Dmytro Borovyk, Serhii Lienkov, Oleh Borovyk, Administration of the State Border Guard Service of Ukraine, Oleksandr Seliukov, Iryna Panibrat

Institutions: Uniwersytet Komisji Edukacji Narodowej w Krakowie, Khmelnytskyi National University, Taras Shevchenko National University of Kyiv, Xi'an Jiaotong University, Bohdan Khmelnytskyi National Academy of the State Border Guard Service of Ukraine

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: gold

OpenAlex ID: W7084080850

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