МОДЕЛИ ОБНАРУЖЕНИЯ ФЕЙКОВОГО КОНТЕНТА С ИСПОЛЬЗОВАНИЕМ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА
Abstract
В современную эпоху стремительного развития цифровых технологий и социальных сетей пользователи получают мгновенный доступ к новостям, при этом не всегда обращая внимание на их достоверность. В результате объём фейковых новостей значительно увеличивается. Фейковый контент представляет собой серьёзную угрозу для общества, поскольку оказывает негативное влияние на политические процессы, экономическую стабильность и социальное взаимодействие. Наиболее интенсивное распространение недостоверная информация получает через социальные сети и другие цифровые платформы.
В данной обзорной статье представлены современные методы обнаружения фейковых новостей, основанные на технологиях машинного обучения и глубокого обучения. Проведён всесторонний анализ существующих подходов, выполнена их сравнительная оценка, а также рассмотрены существующие ограничения, нерешённые задачи и перспективы дальнейших исследований. В обзор включены публикации за период с 2018 по 2025 годы от ведущих научных издателей, таких как IEEE, Intelligent Systems, EMNLP, ACM, Springer, Elsevier, JAIR и других.
References
1. Aphiwongsophon, S.; Chongstitvatana, P. Detecting Fake News with Machine Learning Method. In Proceedings of the 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Chiang Rai, Thailand, 18–21 July 2018; pp. 528–531.
2. Krishna, I.; Kumar, S. Fake News Detection using Naïve Bayes Classifier. Int. J. Creat. Res. Thought (IJCRT) 2021, 9, e757–e761. Available online: https://ijcrt.org/papers/IJCRT2106550.pdf (accessed on 26 May 2025).
3. Ni, B.; Guo, Z.; Li, J.; Jiang, M. Improving Generalizability of Fake News Detection Methods using Propensity Score Matching. arXiv 2020, arXiv:2002.00838.
4. Singh, D.; Khan, A.H.; Meena, S. Fake News Detection Using Ensemble Learning Models. In Proceedings of the Data Analytics and Management. ICDAM 2023; Lecture Notes in Networks and Systems. Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B., Eds.; Springer: Berlin/Heidelberg, Germany, 2023; Volume 78, pp. 55–63.
5. Jiang, T.; Li, J.P.; Haq, A.U.; Saboor, A.; Ali, A. A novel stacking approach for accurate detection of fake news. IEEE Access 2021, 9,22626–22639.
6. Gereme, F.; Zhu, W.; Ayall, T.; Alemu, D. Combating fake news in “low-resource” languages: Amharic fake news detection accompanied by resource crafting. Information 2021, 12, 20.
7. Pardamean, A.; Pardede, H.F. Tuned bidirectional encoder representations from transformers for fake news detection. Indones. J. Electr. Eng. Comput. Sci. 2021, 22, 1667–1671.
8. Kaliyar, R.K.; Goswami, A.; Narang, P. Multiclass Fake News Detection using Ensemble Machine Learning. In Proceedings of the 2019 IEEE 9th International Conference on Advanced Computing (IACC), Tiruchirappalli, India, 13–14 December 2019; pp. 103–107.
9. Albahr, A.; Albahar, M. An Empirical Comparison of Fake News Detection using different Machine Learning Algorithms. Int. J. Adv. Comput. Sci. Appl. 2020, 11, 146–152.
10. Goldani, M.H.; Momtazi, S.; Safabakhsh, R. Detecting fake news with capsule neural networks. Appl. Soft Comput. 2021, 101, 106991.
11. Wang, Y.; Wang, L.; Yang, Y.; Lian, T. Sem-Seq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection. Expert Syst. Appl. 2021, 166, 114090.
12. Ozbay, F.A.; Alatas, B. A novel approach for detection of fake news on social media using metaheuristic optimization algorithms. Elektron. Ir. Elektrotechnika 2019, 25, 62–67.
13. Birunda, S.S.; Devi, R.K. A Novel Score-Based Multi-Source Fake News Detection using Gradient Boosting Algorithm. In Proceedings of the 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore, India, 25–27 March 2021; pp. 406–414.
14. Mugdha, S.B.S.; Ferdous, S.M.; Fahmin, A. Evaluating machine learning algorithms for bengali fake news detection. In Proceedings of the 23rd International Conference on Computer and Information Technology (ICCIT), DHAKA, Bangladesh, 19–21 December 2020; pp. 1–6.
15. Al-Ahmad, B.; Al-Zoubi, A.M.; Abu Khurma, R.; Aljarah, I. An evolutionary fake news detection method for COVID-19 pandemic information. Symmetry 2021, 13, 1091.
16. Jardaneh, G.; Abdelhaq, H.; Buzz, M.; Johnson, D. Classifying Arabic tweets based on credibility using content and user features. In Proceedings of the 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), Amman, Jordan, 9–11 April 2019; pp. 596–601.
17. Alshuwaier, F.; Areshey, A.; Poon, J. Applications and Enhancement of Document-Based Sentiment Analysis in Deep learning Methods: Systematic Literature Review. Intell. Syst. Appl. 2022, 15, 200090.
18. Battal, B.; Yıldırım, B.; Dinçaslan, Ö.F.; Cicek, G. Fake News Detection with Machine Learning Algorithms. Celal Bayar Univ. J. Sci. 2024, 20, 65–83.
19. Kitchenhamy, B.; Brereton, O.; Budgen, D.; Turner, M.; Bailey, J.; Linkman, S. Systematic Literature Reviews in Software Engineering-A Systematic Literature Review; Elsevier: Amsterdam, The Netherlands, 2009; Volume 51, pp. 7–15.
20. Do, H.H.; Prasad, P.; Maag, A.; Alsadoon, A. Deep learning for aspect-based sentiment analysis: A comparative review. Expert Syst. Appl. 2019, 118, 272–299.
21. Toyer, S.; Thiebaux, S.; Trevizan, F.; Xie, L. Asnets: Deep learning for generalised planning. J. Artif. Intell. Res. (JAIR) 2020, 68, 1–68.
22. Tiwari, S.; Jain, S. Fake News Detection Using Machine Learning Algorithms. In Proceedings of the KILBY 100 7th International Conference on Computing Sciences 2023 (ICCS 2023), Phagwara, India, 5 May 2024.
23. Rampurkar, M.V.; Thirupurasundari, D.D. An Approach towards Fake News Detection using Machine Learning Techniques. Int. J. Intell. Syst. Appl. Eng. 2024, 12, 2868–2874.
24. Murti, H.; Sulastri, S.; Santoso, D.B.; Diartono, D.A.; Nugroho, K. Design of Intelligent Model for Text-Based Fake News Detection Using K-Nearest Neighbor Method. Sinkron 2025, 9, 1–7.
25. Nagoudi, E.M.; Elmadany, A.; Abdul-Mageed, M.; Alhindi, T.; Cavusoglu, H. Machine Generation and Detection of Arabic Manipulated and Fake News. In Workshop on Arabic Natural Language Processing; Association for Computational Linguistics: Stroudsburg, PA, USA, 2020.
26. Hamed, S.K.; Ab Aziz, M.J.; Yaakub, M.R. Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users’ Comments. Sensors 2023, 23, 1748.
27. Verma, P.K.; Agrawal, P.; Amorim, I.; Prodan, R. WELFake: Word Embedding Over Linguistic Features for Fake News Detection. IEEE Trans. Comput. Soc. Syst. 2021, 8, 881–893.
28. Ivancova, K.; Sarnovsky, M.; Krešnˇáková, V. Fake news detection in Slovak language using deep learning techniques. In Proceedings of the SAMI 2021, IEEE 19th World Symposium on Applied Machine Intelligence and Informatics, Herl’any, Slovakia, 21–23 January 2021; pp. 000255–000260.
29. Subramanian, M.; Premjith, B.; Shanmugavadivel, K.; Pandiyan, S.; Palani, B.; Chakravarthi., B. Overview of the Shared Task on Fake News Detection in Dravidian Languages-DravidianLangTech@NAACL 2025. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, Acoma, The Albuquerque Convention Center, Albuquerque, NM, USA, 3 May 2025; Association for Computational Linguistics (ACL): Stroudsburg, PA, USA, 2025; pp. 759–767.
30. Jingyuan, Y.; Zeqiu, X.; Tianyi, H.; Peiyang, Y. Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions. Comput. Lang. 2025, 87–93.
31. Tan, M.; Bakır, H. Fake News Detection Using BERT and Bi-LSTM with Grid Search Hyperparameter Optimization. Bilis¸im Teknolojileri Dergisi. 2025, 18, 11–28.
32. Alsuwat, E.; Alsuwat, H. An improved multi-modal framework for fake news detection using NLP and Bi-LSTM. J. Supercomput. 2025, 81, 177.
33. Mouratidis, D.; Kanavos, A.; Kermanidis, K. From Misinformation to Insight: Machine Learning Strategies for Fake News Detection. Information 2025, 16, 189.
34. Al-Tarawneh, M.A.B.; Al-Irr, O.; Al-Maaitah, K.S.; Kanj, H.; Aly, W.H.F. Enhancing Fake News Detection with Word Embedding: A Machine Learning and Deep Learning Approach. Computers 2024, 13, 239.
35. Shen, L.; Long, Y.; Cai, X.; Razzak, I.; Chen, G.; Liu, K.; Jameel, S. GAMED: Knowledge Adaptive Multi- Experts Decoupling for Multimodal Fake News Detection. In Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining (WSDM ’25), Hannover, Germany, 10–14 March 2025; pp. 586–595.
36. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Aki, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71.
