Approach to the fake news detection using the graph neural networks
Main Article Content
Abstract
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.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Accepted 2023-05-06
Published 2023-05-16
References
Allcott, H. and Gentzkow, M., 2017. Social Media and Fake News in the 2016 Election. Journal of Economic Perspectives, 31(2), pp.211–36. Available from: https://doi.org/10.1257/jep.31.2.211. DOI: https://doi.org/10.1257/jep.31.2.211
Berinsky, A.J., 2017. Rumors and Health Care Reform: Experiments in Political Misinformation British Journal of Political Science, 47(2), p.241–262. Available from: https://doi.org/10.1017/S0007123415000186. DOI: https://doi.org/10.1017/S0007123415000186
Dou, Y., Shu, K., Xia, C., Yu, P.S. and Sun, L., 2021. User Preference-Aware Fake News Detection. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: Association for Computing Machinery, SIGIR ’21, p.2051–2055. Available from: https://doi.org/10.1145/3404835.3462990. DOI: https://doi.org/10.1145/3404835.3462990
Giménez-García, J.M., Duarte, M.C., Zimmermann, A., Gravier, C., Jr., E.R.H. and Maret, P., 2018. NELL2RDF: Reading the Web, Tracking the Provenance, and Publishing it as Linked Data. In: S. Capadisli, F. Cotton, J.M. Giménez-García, A. Haller, E. Kalampokis, V. Nguyen, A.P. Sheth and R. Troncy, eds. Joint Proceedings of the International Workshops on Contextualized Knowledge Graphs, and Semantic Statistics co-located with 17th International Semantic Web Conference (ISWC 2018). CEUR-WS.org, CEUR Workshop Proceedings, vol. 2317. Available from: https://ceur-ws.org/Vol-2317/article-02.pdf.
Hamilton,W.L., Ying, R. and Leskovec, J., 2017. Inductive Representation Learning on Large Graphs. Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY, USA: Curran Associates Inc., NIPS’17, p.1025–1035. Available from: https://doi.org/10.5555/3294771.3294869.
Hernández, D., Hogan, A. and Krötzsch, M., 2015. Reifying RDF: What Works Well With Wikidata? In: T. Liebig and A. Fokoue, eds. Proceedings of the 11th International Workshop on Scalable Semantic Web Knowledge Base Systems co-located with 14th International Semantic Web Conference (ISWC 2015), Bethlehem, PA, USA, October 11, 2015. CEUR-WS.org, CEUR Workshop Proceedings, vol. 1457, pp.32–47. Available from: https://ceur-ws.org/Vol-1457/SSWS2015_paper3.pdf.
Howard, G.E. and Czekaj, M., eds, 2019. Russia’s military strategy and doctrine. Jamestown Foundation, pp.159–184. DOI: https://doi.org/10.1515/9781735275284
Jentzsch, A., 2014. Linked Open Data Cloud. In: T. Pellegrini, H. Sack and S. Auer, eds. Linked Enterprise Data: Management und Bewirtschaftung vernetzter Unternehmensdaten mit Semantic Web Technologien. Berlin, Heidelberg: Springer Berlin Heidelberg, X.media.press, pp.209–219. Available from: https://doi.org/10.1007/978-3-642-30274-9_10. DOI: https://doi.org/10.1007/978-3-642-30274-9_10
Levchenko, O.V. and Kosogov, O.M., 2016. Methods of identification of events of negative information influence based on the analysis of open sources. Systemy obrobky informatsii, 1, pp.100–102. Available from: http://nbuv.gov.ua/UJRN/soi_2016_1_22.
Mayank, M., Sharma, S. and Sharma, R., 2022. DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection. 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). pp.47–51. Available from: https://doi.org/10.1109/ASONAM55673.2022.10068653. DOI: https://doi.org/10.1109/ASONAM55673.2022.10068653
Mendes, P.N., Jakob, M., García-Silva, A. and Bizer, C., 2011. DBpedia Spotlight: Shedding Light on theWeb of Documents. Proceedings of the 7th International Conference on Semantic Systems. New York, NY, USA: Association for Computing Machinery, I-Semantics ’11, p.1–8. Available from: https://doi.org/10.1145/2063518.2063519. DOI: https://doi.org/10.1145/2063518.2063519
Pilkevych, I., Fedorchuk, D., Naumchak, O. and Romanchuk, M., 2021. Fake News Detection in the Framework of Decision-Making System through Graph Neural Network. 2021 IEEE 4th International Conference on Advanced Information and Communication Technologies (AICT). pp.153–157. Available from: https://doi.org/10.1109/AICT52120.2021.9628907. DOI: https://doi.org/10.1109/AICT52120.2021.9628907
Pilkevych, I., Fedorchuk, D., Romanchuk, M. and Naumchak, O., 2023. An analysis of approach to the fake news assessment based on the graph neural networks. Proceedings of the 3rd Edge Computing Workshop. CEUR Workshop Proceedings, doors 2023, pp.56–65. Available from: https://www.ceur-ws.org/Vol-3374/paper04.pdf. DOI: https://doi.org/10.55056/jec.592
Rebele, T., Suchanek, F., Hoffart, J., Biega, J., Kuzey, E. and Weikum, G., 2016. YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames. The Semantic Web – ISWC 2016: 15th International Semantic Web Conference, Kobe, Japan, October 17–21, 2016, Proceedings, Part II. Berlin, Heidelberg: Springer-Verlag, p.177–185. Available from: https://doi.org/10.1007/978-3-319-46547-0_19. DOI: https://doi.org/10.1007/978-3-319-46547-0_19
Ruiz, L., Gama, F. and Ribeiro, A., 2020. Gated graph recurrent neural networks. IEEE Transactions on Signal Processing, 68, pp.6303–6318. Available from: https://doi.org/10.1109/tsp.2020.3033962. DOI: https://doi.org/10.1109/TSP.2020.3033962
Salehi, A. and Davulcu, H., 2020. Graph attention auto-encoders. 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI). pp.989–996. Available from: https://doi.org/10.1109/ICTAI50040.2020.00154. DOI: https://doi.org/10.1109/ICTAI50040.2020.00154
Stratehiia informatsiinoi bezpeky, 2021. Available from: https://www.president.gov.ua/documents/6852021-41069.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P. and Bengio, Y., 2018. Graph Attention Networks. 1710.10903.
Waagmeester, A., Stupp, G., Burgstaller-Muehlbacher, S., Good, B.M., Griffith, M., Griffith, O.L., Hanspers, K., Hermjakob, H., Hudson, T.S., Hybiske, K., Keating, S.M., Manske, M., Mayers, M., Mietchen, D., Mitraka, E., Pico, A.R., Putman, T., Riutta, A., Queralt-Rosinach, N., Schriml, L.M., Shafee, T., Slenter, D., Stephan, R., Thornton, K., Tsueng, G., Tu, R., Ul-Hasan, S., Willighagen, E., Wu, C. and Su, A.I., 2020. Science Forum: Wikidata as a knowledge graph for the life sciences. eLife, 9, p.e52614. Available from: https://doi.org/10.7554/eLife.52614. DOI: https://doi.org/10.7554/eLife.52614
Woolley, S. and Howard, P., 2016. Automation, Algorithms, and Politics| Political Communication, Computational Propaganda, and Autonomous Agents — Introduction. International Journal of Communication, 10. Available from: https://ijoc.org/index.php/ijoc/article/view/6298.
Zonghan, W., Shirui, P., Fengwen, C., Guodong, L., Chengqi, Z. and S., Y.P., 2021. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), pp.4–24. Available from: https://doi.org/10.1109/TNNLS.2020.2978386. DOI: https://doi.org/10.1109/TNNLS.2020.2978386