DOI: https://doi.org/10.32515/2664-262X.2025.12(43).1.90-98
Identifying Sources and Participants of Propaganda in TikTok Using Machine Learning
About the Authors
Olga Lozynska, Associate Professor, PhD in Information Technology (Candidate of Technical Sciences), Associate Professor of the Department of Information Systems and Networks, Lviv Polytechnic National University, Lviv, Ukraine, ORCID: 0000-0002-5079-0544, e-mail: olha.v.lozynska@lpnu.ua
Oksana Markiv, Associate Professor, PhD in Information Technology (Candidate of Technical Sciences), Associate Professor of the Department of Information Systems and Networks, Lviv Polytechnic National University, Lviv, Ukraine, ORCID: 0000-0002-1691-1357, e-mail: oksana.o.markiv@lpnu.ua
Victoria Vysotska, Associate Professor, Doctor of Technical Sciences, Professor of the Department of Information Systems and Networks, Lviv Polytechnic National University, Lviv, Ukraine, ORCID: 0000-0001-6417-3689, e-mail: victoria.a.vysotska@lpnu.ua
Abstract
The purpose of this work is to establish a unified evaluation framework for Virtual Reality (VR) resilience that guarantees continuous operation, data integrity and seamless user experience under varied conditions. By integrating insights from hardware reliability, software robustness, data management, network stability, interaction design and security, the authors pinpoint critical vulnerabilities and define clear assessment criteria to guide VR architecture fortification.
The authors survey leading resilience techniques across six domains. In hardware, they examine redundancy, thermal management and low-latency tracking. Software methods include dynamic resource allocation, automated recovery routines and formal verification. Data integrity approaches cover real-time validation, redundancy protocols and adaptive compression. Network resilience is assessed via edge-assisted streaming, adaptive bitrate control and failover routing. Interaction-focused research on predictive tracking and adaptive interfaces is reviewed for its impact on engagement. Security measures such as multi-factor authentication, end-to-end encryption and AI-driven threat detection are evaluated alongside emerging quantum cryptography and hybrid cloud-edge architectures. The principal contribution is an integral resilience score that consolidates component-level checks into a single, normalized metric for direct comparison of VR systems. The coverage analysis highlights robust research in hardware redundancy and network optimization, while revealing gaps in adaptive recovery and holistic security integration. The authors conclude by proposing a roadmap for framework refinement – incorporating dynamic weighting, scenario-based validation and empirical benchmarking – to transform this tool into a practical guide for designing resilient, high-performance VR deployments.
Keywords
disinformation, propaganda sources, dataset, RoBERTa model, clustering, potential propaganda participants, set of criteria for identifying propaganda participants
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References
1. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). Roberta: A robustly optimized Bert pretraining approach. arXiv preprint arXiv:1907.11692. DOI: 10.48550/arXiv.1907.11692.
2. Arif, M., Tonja, A. L., Ameer, I., Kolesnikova, O., Gelbukh, A., Sidorov, G., & Meque, A. G. M. (2022). CIC at CheckThat! 2022: Multi-class and Cross-lingual Fake News Detection. CEUR Workshop Proceedings (pp. 434 – 443).
3. Kalraa, S., Verma, P., Sharma, Y., & Chauhan, G. S. (2021). Ensembling of Various Transformer Based Models for the Fake News Detection Task in the Urdu Language. Proceedings of the Forum for Information Retrieval Evaluation.
4. Prytula, M. (2024). Fine-tuning BERT, DistilBERT, XLM-RoBERTa, and Ukr-RoBERTa models for sentiment analysis of Ukrainian language reviews. Artificial Intelligence, 29(2), 85–97. https://doi.org/10.15407/jai2024.02.085
5. Panchenko, D., Tytarenko, S., et al. (2022). Evaluation and Analysis of the NLP Model Zoo for Ukrainian Text Classification. In Information and Communication Technologies in Education, Research, and Industrial Applications (pp. 109–123). Springer. https://doi.org/10.1007/978-3-031-20834-8_6.
6. Dementieva, D., Babakov, N., & Fraser, A. (2025, May 29). EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian. arXiv preprint. https://doi.org/10.48550/arXiv.2505.23297.
7. Saif M. Mohammad (2022). Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis. Computational Linguistics, 48(2), 239–278. https://doi.org/10.48550/arXiv.2109.08256.
8. Shynkarov, Y., Solopova, V., & Schmitt, V. (2025). Improving Sentiment Analysis for Ukrainian Social Media Code-Switching Data. In Proceedings of UNLP-2025 Workshop (COSMUS benchmark).
9. Haltiuk, M., & Smywiński-Pohl, A. (2024). LiBERTa: Advancing Ukrainian Language Modeling through Pre-training from Scratch. In Proceedings of the Third Ukrainian Natural Language Processing Workshop (pp. 120–128).
10. Dorenskyi, O.P., Ulichev, O.S., Zadorozhnyi, K.O., Kovalenko, A.S., & Dreeva, G.M. (2024). The conceptual model of the information counteraction system of the coordination center for national security and defense issues. Central Ukrainian Scientific Bulletin. Technical Sciences, 10(41), Part 2, 23-31. DOI: 10.32515/2664-262X.2024.10(41).2.23-31.
11. Lozynska, O.V., Markiv, O.O., & Vysotska, V.A. (2025). Method for detecting sources of disinformation based on ensemble machine learning models. Bionics of Intelligence, 1(102), 11–19. DOI: 10.30837/ bi.2025.1(102).02.
Citations
1. Liu Y., Ott M., Goyal N., Du J., Joshi M., Chen D., Levy O., Lewis M., Zettlemoyer L., Stoyanov V. Roberta: A robustly optimized Bert pretraining approach. 2019. arXiv preprint arXiv:1907.11692. URL: https://doi.org/10.48550/arXiv.1907.11692.
2. Arif M., Tonja A. L., Ameer I., Kolesnikova O., Gelbukh A., Sidorov G., Meque A. G. M. CIC at CheckThat! 2022: Multi-class and Cross-lingual Fake News Detection. CEUR Workshop Proceedings 2022. Pp. 434 – 443.
3. Kalraa S., Vermas P., Sharma Y., Chauhan G. S. Ensembling of Various Transformer Based Models for the Fake News Detection Task in the Urdu Language. Proceedings of the Forum for Information Retrieval Evaluation. 2021.
4. Prytula M. Fine-tuning BERT, DistilBERT, XLM-RoBERTa, and Ukr-RoBERTa models for sentiment analysis of Ukrainian language reviews. Artificial Intelligence. 2024. № 29(2). Pp. 85–97. URL: https://doi.org/10.15407/jai2024.02.085.
5. Panchenko D., Tytarenko S., et al. Evaluation and Analysis of the NLP Model Zoo for Ukrainian Text Classification. In Information and Communication Technologies in Education, Research, and Industrial Applications. Springer. 2022. Pp. 109–123. URL: https://doi.org/10.1007/978-3-031-20834-8_6.
6. Dementieva D., Babakov N., Fraser A. EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian. arXiv preprint. 2025. URL: https://doi.org/10.48550/arXiv.2505.23297.
7. Saif M. Mohammad. Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis. Computational Linguistics. 2022. № 48(2). Pp. 239–278. URL: https://doi.org/10.48550/arXiv.2109.08256.
8. Shynkarov Y., Solopova V., Schmitt V. Improving Sentiment Analysis for Ukrainian Social Media Code-Switching Data. In Proceedings of UNLP-2025 Workshop (COSMUS benchmark). 2025.
9. Haltiuk M., Smywiński-Pohl A. LiBERTa: Advancing Ukrainian Language Modeling through Pre-training from Scratch. In Proceedings of the Third Ukrainian Natural Language Processing Workshop. 2024. Pp. 120–128.
10. Доренський О.П., Улічев О.С., Задорожний К.О., Коваленко А.С., Дрєєва Г.М. Концептуальна модель системи інформаційного протиборства координаційного центру з питань національної безпеки і оборони. Центральноукраїнський науковий вісник. Технічні науки. 2024. Вип. 10(41). Ч. 2. С. 23-31. URL: https://doi.org/10.32515/2664-262X.2024.10(41).2.23-31.
11. Лозинська О.В., Марків О.О., Висоцька В.А. Метод виявлення джерел дезінформації на основі ансамблевих моделей машинного навчання. Біоніка інтелекту. 2025. № 1 (102). С. 11-19. URL: https://doi.org/10.30837/ bi.2025.1(102).02.
Copyright (©) 2025, Olga Lozynska, Oksana Markiv, Victoria Vysotska
Identifying Sources and Participants of Propaganda in TikTok Using Machine Learning
About the Authors
Olga Lozynska, Associate Professor, PhD in Information Technology (Candidate of Technical Sciences), Associate Professor of the Department of Information Systems and Networks, Lviv Polytechnic National University, Lviv, Ukraine, ORCID: 0000-0002-5079-0544, e-mail: olha.v.lozynska@lpnu.ua
Oksana Markiv, Associate Professor, PhD in Information Technology (Candidate of Technical Sciences), Associate Professor of the Department of Information Systems and Networks, Lviv Polytechnic National University, Lviv, Ukraine, ORCID: 0000-0002-1691-1357, e-mail: oksana.o.markiv@lpnu.ua
Victoria Vysotska, Associate Professor, Doctor of Technical Sciences, Professor of the Department of Information Systems and Networks, Lviv Polytechnic National University, Lviv, Ukraine, ORCID: 0000-0001-6417-3689, e-mail: victoria.a.vysotska@lpnu.ua
Abstract
Keywords
Full Text:
PDFReferences
1. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). Roberta: A robustly optimized Bert pretraining approach. arXiv preprint arXiv:1907.11692. DOI: 10.48550/arXiv.1907.11692.
2. Arif, M., Tonja, A. L., Ameer, I., Kolesnikova, O., Gelbukh, A., Sidorov, G., & Meque, A. G. M. (2022). CIC at CheckThat! 2022: Multi-class and Cross-lingual Fake News Detection. CEUR Workshop Proceedings (pp. 434 – 443).
3. Kalraa, S., Verma, P., Sharma, Y., & Chauhan, G. S. (2021). Ensembling of Various Transformer Based Models for the Fake News Detection Task in the Urdu Language. Proceedings of the Forum for Information Retrieval Evaluation.
4. Prytula, M. (2024). Fine-tuning BERT, DistilBERT, XLM-RoBERTa, and Ukr-RoBERTa models for sentiment analysis of Ukrainian language reviews. Artificial Intelligence, 29(2), 85–97. https://doi.org/10.15407/jai2024.02.085
5. Panchenko, D., Tytarenko, S., et al. (2022). Evaluation and Analysis of the NLP Model Zoo for Ukrainian Text Classification. In Information and Communication Technologies in Education, Research, and Industrial Applications (pp. 109–123). Springer. https://doi.org/10.1007/978-3-031-20834-8_6.
6. Dementieva, D., Babakov, N., & Fraser, A. (2025, May 29). EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian. arXiv preprint. https://doi.org/10.48550/arXiv.2505.23297.
7. Saif M. Mohammad (2022). Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis. Computational Linguistics, 48(2), 239–278. https://doi.org/10.48550/arXiv.2109.08256.
8. Shynkarov, Y., Solopova, V., & Schmitt, V. (2025). Improving Sentiment Analysis for Ukrainian Social Media Code-Switching Data. In Proceedings of UNLP-2025 Workshop (COSMUS benchmark).
9. Haltiuk, M., & Smywiński-Pohl, A. (2024). LiBERTa: Advancing Ukrainian Language Modeling through Pre-training from Scratch. In Proceedings of the Third Ukrainian Natural Language Processing Workshop (pp. 120–128).
10. Dorenskyi, O.P., Ulichev, O.S., Zadorozhnyi, K.O., Kovalenko, A.S., & Dreeva, G.M. (2024). The conceptual model of the information counteraction system of the coordination center for national security and defense issues. Central Ukrainian Scientific Bulletin. Technical Sciences, 10(41), Part 2, 23-31. DOI: 10.32515/2664-262X.2024.10(41).2.23-31.
11. Lozynska, O.V., Markiv, O.O., & Vysotska, V.A. (2025). Method for detecting sources of disinformation based on ensemble machine learning models. Bionics of Intelligence, 1(102), 11–19. DOI: 10.30837/ bi.2025.1(102).02.
Citations
1. Liu Y., Ott M., Goyal N., Du J., Joshi M., Chen D., Levy O., Lewis M., Zettlemoyer L., Stoyanov V. Roberta: A robustly optimized Bert pretraining approach. 2019. arXiv preprint arXiv:1907.11692. URL: https://doi.org/10.48550/arXiv.1907.11692.
2. Arif M., Tonja A. L., Ameer I., Kolesnikova O., Gelbukh A., Sidorov G., Meque A. G. M. CIC at CheckThat! 2022: Multi-class and Cross-lingual Fake News Detection. CEUR Workshop Proceedings 2022. Pp. 434 – 443.
3. Kalraa S., Vermas P., Sharma Y., Chauhan G. S. Ensembling of Various Transformer Based Models for the Fake News Detection Task in the Urdu Language. Proceedings of the Forum for Information Retrieval Evaluation. 2021.
4. Prytula M. Fine-tuning BERT, DistilBERT, XLM-RoBERTa, and Ukr-RoBERTa models for sentiment analysis of Ukrainian language reviews. Artificial Intelligence. 2024. № 29(2). Pp. 85–97. URL: https://doi.org/10.15407/jai2024.02.085.
5. Panchenko D., Tytarenko S., et al. Evaluation and Analysis of the NLP Model Zoo for Ukrainian Text Classification. In Information and Communication Technologies in Education, Research, and Industrial Applications. Springer. 2022. Pp. 109–123. URL: https://doi.org/10.1007/978-3-031-20834-8_6.
6. Dementieva D., Babakov N., Fraser A. EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian. arXiv preprint. 2025. URL: https://doi.org/10.48550/arXiv.2505.23297.
7. Saif M. Mohammad. Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis. Computational Linguistics. 2022. № 48(2). Pp. 239–278. URL: https://doi.org/10.48550/arXiv.2109.08256.
8. Shynkarov Y., Solopova V., Schmitt V. Improving Sentiment Analysis for Ukrainian Social Media Code-Switching Data. In Proceedings of UNLP-2025 Workshop (COSMUS benchmark). 2025.
9. Haltiuk M., Smywiński-Pohl A. LiBERTa: Advancing Ukrainian Language Modeling through Pre-training from Scratch. In Proceedings of the Third Ukrainian Natural Language Processing Workshop. 2024. Pp. 120–128.
10. Доренський О.П., Улічев О.С., Задорожний К.О., Коваленко А.С., Дрєєва Г.М. Концептуальна модель системи інформаційного протиборства координаційного центру з питань національної безпеки і оборони. Центральноукраїнський науковий вісник. Технічні науки. 2024. Вип. 10(41). Ч. 2. С. 23-31. URL: https://doi.org/10.32515/2664-262X.2024.10(41).2.23-31.
11. Лозинська О.В., Марків О.О., Висоцька В.А. Метод виявлення джерел дезінформації на основі ансамблевих моделей машинного навчання. Біоніка інтелекту. 2025. № 1 (102). С. 11-19. URL: https://doi.org/10.30837/ bi.2025.1(102).02.
Copyright (©) 2025, Olga Lozynska, Oksana Markiv, Victoria Vysotska