Approach to the fake news detection using the graph neural networks
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 informationa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Academy of Cognitive and Natural Sciences
2023-05-01
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| Series: | Journal of Edge Computing |
| Subjects: | |
| Online Access: | https://acnsci.org/journal/index.php/jec/article/view/592 |
| _version_ | 1797361812683882496 |
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| author | Ihor A. Pilkevych Dmytro L. Fedorchuk Mykola P. Romanchuk Olena M. Naumchak |
| author_facet | Ihor A. Pilkevych Dmytro L. Fedorchuk Mykola P. Romanchuk Olena M. Naumchak |
| author_sort | Ihor A. Pilkevych |
| collection | DOAJ |
| description |
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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| first_indexed | 2024-03-08T15:59:45Z |
| format | Article |
| id | doaj.art-a21763a13f0e41d4823a02703fae4909 |
| institution | Directory Open Access Journal |
| issn | 2837-181X |
| language | English |
| last_indexed | 2024-03-08T15:59:45Z |
| publishDate | 2023-05-01 |
| publisher | Academy of Cognitive and Natural Sciences |
| record_format | Article |
| series | Journal of Edge Computing |
| spelling | doaj.art-a21763a13f0e41d4823a02703fae49092024-01-08T12:04:37ZengAcademy of Cognitive and Natural SciencesJournal of Edge Computing2837-181X2023-05-012110.55056/jec.592Approach to the fake news detection using the graph neural networksIhor A. Pilkevych0Dmytro L. Fedorchuk1Mykola P. Romanchuk2Olena M. Naumchak3Korolyov Zhytomyr Military InstituteKorolyov Zhytomyr Military InstituteKorolyov Zhytomyr Military InstituteKorolyov Zhytomyr Military Institute 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. https://acnsci.org/journal/index.php/jec/article/view/592graph neural networkspsychological influencesfake newsknowledge graphinformation messagesonline media |
| spellingShingle | Ihor A. Pilkevych Dmytro L. Fedorchuk Mykola P. Romanchuk Olena M. Naumchak Approach to the fake news detection using the graph neural networks Journal of Edge Computing graph neural networks psychological influences fake news knowledge graph information messages online media |
| title | Approach to the fake news detection using the graph neural networks |
| title_full | Approach to the fake news detection using the graph neural networks |
| title_fullStr | Approach to the fake news detection using the graph neural networks |
| title_full_unstemmed | Approach to the fake news detection using the graph neural networks |
| title_short | Approach to the fake news detection using the graph neural networks |
| title_sort | approach to the fake news detection using the graph neural networks |
| topic | graph neural networks psychological influences fake news knowledge graph information messages online media |
| url | https://acnsci.org/journal/index.php/jec/article/view/592 |
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