Approach to the fake news detection using the graph neural networks

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Ihor A. Pilkevych
Dmytro L. Fedorchuk
Mykola P. Romanchuk
Olena M. Naumchak


The experience of Russia’s war against Ukraine demonstrates the relevance and necessity of understanding the problems of constant disinformation, the spread of propaganda, and the implementation of destructive negative psychological influence. The issue of dissemination in online media informational messages containing negative psychological influence was researched. Ways of improving the system of monitoring online media using the graph neural networks are considered. The methods of automated fake news detection, based on graph neural networks, were reviewed. The purpose of the article is the analysis of existing approaches that allow identifying destructive signs of influence in text data. It is found that the best way to automate the content analysis process is to use the latest machine learning methods. It was determined and substantiated that graph neural networks are the most reliable and effective solution for the specified task. An approach to automating this procedure based on graph neural networks has been designed and analyzed, which will allow timely and efficient detection and analysis of fake news in the information space of our country. During the research, the process of detecting fake news was simulated. The obtained results showed that the described models of graph neural networks can provide good results in solving the tasks of timely detection and response to threats posed by fake news spread by Russia.


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How to Cite
Pilkevych, I.A., Fedorchuk, D.L., Romanchuk, M.P. and Naumchak, O.M., 2023. Approach to the fake news detection using the graph neural networks. Journal of Edge Computing [Online], 2(1), pp.24–36. Available from: [Accessed 23 May 2024].

How to Cite

Pilkevych, I.A., Fedorchuk, D.L., Romanchuk, M.P. and Naumchak, O.M., 2023. Approach to the fake news detection using the graph neural networks. Journal of Edge Computing [Online], 2(1), pp.24–36. Available from: [Accessed 23 May 2024].
Received 2023-03-21
Accepted 2023-05-06
Published 2023-05-16


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