DOI: https://doi.org/10.32515/2664-262X.2025.11(42).2.70-78
A Method for Detecting Disinformation Spreaders Based on a Graph Representation of the Social Network Structure
About the Authors
Olga Lozynska, Associate Professor, PhD in Information Technology (Candidate of Technical Sciences), Associate Professor of Information Systems and Networks Department, 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 Information Systems and Networks Department, Lviv Polytechnic National University, Lviv, Ukraine, ORCID:0000-0002-1691-1357, e-mail: oksana.o.markiv@lpnu.ua
Victoria Vysotska, Associate Professor, Doctor in Information Technology (Doctor of Technical Sciences), Professor of Information Systems and Networks Department, Lviv Polytechnic National University, Lviv, Ukraine, ORCID: 0000-0001-6417-3689, e-mail: victoria.a.vysotska@lpnu.ua
Abstract
The paper considers the problem of disinformation dissemination and detection in social networks. Based on the analysis, it was found that the problem of disinformation detection is closely related, first of all, to the problem of detecting disinformation distributors. A method for detecting disinformation spreaders is proposed based on the representation of a social network in the form of a directed graph, with the division of users into communities.
In the study, the social network is presented as a connected directed graph, where each node represents a user. Users are connected to each other by edges and are united into different communities. The level of trust within a community is much higher than between users of different communities. Thus, if misinformation spreads within such a community, the probability of infection of all members of the community will be high. We introduce a classification of nodes (neighboring, border, and main) and show the role they play in the dissemination of information. An analysis of a Ukrainian-language news dataset, including over 2000 records, is conducted to identify users who can potentially become disseminators of information (both true and false). The number of items with the label “true” is 1283, and with the label “fake” is 891. We need to predict the boundary nodes, which are most likely to become information spreaders in the user community, since they are most tangential to all other nodes in the community. In the same way, calculations need to be made to predict the main nodes, which are most likely to become information distributors within the community itself. Thus, if disinformation reaches the boundary nodes (there are 11 of them in our dataset), it will spread throughout the community. In addition, it was found that users who have fake posts on the page are most likely to spread fake information. This may indicate that this account is fake.
This study introduces a method for identifying misinformation spreaders in online social networks. The findings highlight that boundary nodes serve as conduits for inter-community dissemination, while core nodes play a significant role within their respective communities. The result of research using a real-world Ukrainian news dataset demonstrates that users who predominantly share fake posts are more likely to spread disinformation, potentially indicating the use of fake accounts. These insights can inform the design of advanced detection algorithms and support efforts to safeguard the information integrity of digital communication platforms. Further studies are planned to calculate a pair of “trust-reliability” scores for each network node using the Trust in Social Media (TSM) algorithm.
Keywords
disinformation, fake news, dataset, disinformation spreaders, graphs
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References
1. Almaatouq, A., Shmueli, E., Nouh, M., Alabdulkareem, A., Singh, V.K., Alsaleh, M., Alarifi, A., Alfaris, A., & Pentland, A. (2016). If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts. International Journal of Information Security. 15 (5), 475-491. https://doi.org/10.1007/s10207-016-0321-5.
2. Castillo, C., Mendoza, M., & Poblete, B. (2011). Information credibility on Twitter. 20th international conference on World wide web (675–684). https://doi.org/10.1145/1963405.1963500.
3. Pennycook, G., & Rand, D.G. (2020). Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. Journal of Personality. 88(2), 185–200. https://doi.org/10.1111/jopy.12476.
4. Karami, M., Nazer, T.H., & Liu, H. (2021). Profiling fake news spreaders on social media through psychological and motivational factors. 32nd ACM conference on hypertext and social media (225–230). https://doi.org/10.1145/3465336.347509.
5. Shu, K., Wang, S., & Liu, H. (2018). Understanding user profiles on social media for fake news detection. 2018 IEEE conference on multimedia information processing and retrieval (430–435). DOI: 10.1109/MIPR.2018.00092.
6. Shu, K., Wang, S., & Liu, H. (2019). Beyond news contents: the role of social context for fake news detection. Twelfth ACM international conference on web search and data mining (312–320). URL: https://doi.org/10.1145/3289600.3290994.
7. Rangel, F., Giachanou, A., Ghanem, B.H.H., & Rosso, P. (2020). Overview of the 8th author profiling task at pan 2020: profiling fake news spreaders on Twitter. 11th International Conference of the CLEF Association. DOI: 10.1007/978-3-030-58219-7_25.
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10. Vogel, I., & Meghana, M. (2020). Fake news spreader detection on Twitter using character n-grams. Working Notes of Conference and Labs of the Evaluation Forum. https://ceur-ws.org/Vol-2696/paper_59.pdf.
11. Giachanou, A., Ríssola, E.A., Ghanem, B., Crestani, F., & Rosso, P. (2020). The role of personality and linguistic patterns in discriminating between fake news spreaders and fact checkers. International conference on applications of natural language to information systems (181–192). DOI: 10.1007/978-3-030-51310-8_17.
12. Chen, T., Li, X., Yin, H., & Zhang, J. (2018). Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection. Lecture Notes in Computer Science. 11154, 40-52. URL: https://doi.org/10.48550/arXiv.1704.05973.
13. Bian, T., Xiao, X., Xu, T., Zhao, P., Huang, W., Rong, Y., & Huang, J. (2020). Rumor detection on social media with bi-directional graph convolutional networks. AAAI 2020. https://doi.org/10.48550/arXiv.2001.06362.
14. Lu, Y., & Li, C. (2020). GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media. 58th Annual Meeting of the Association for Computational Linguistics (505–514). https://doi.org/10.48550/arXiv.2004.11648 .
15. Nguyen, V., Sugiyama, K., Nakov, P., & Kan, M. (2020). FANG: Leveraging Social Context for Fake News Detection Using Graph Representation. 29th ACM International Conference on Information and Knowledge Management (1165 – 1174). https://doi.org/10.1145/3340531.3412046.
16. Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science. 359 (6380), 1146–1151. https://www.science.org/doi/10.1126/science.aap9559.
17. Kim, J., Kim, D., & Oh, A. (2019). Homogeneity-based transmissive process to model true and false news in social networks. 12th ACM International Conference on Web Search and Data Mining. https://doi.org/10.48550/arXiv.1811.09702.
18. Rath, B., Gao, W., & Srivastava, J. (2019). Evaluating vulnerability to fake news in social networks: a community health assessment model. 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). DOI: 10.1145/3341161.3342920.
19. Lozynska, O., Markiv, O., Vysotska, V., Romanchuk, R., & Nazarkevych, M. (2024). Information technology for developing and populating a disinformation dataset using intelligent deepfakes and clickbait search. Herald of Khmelnytskyi National University. Technical Sciences, 343(6(1)), 158-167. DOI: https://doi.org/10.31891/2307-5732-2024-343-6-24 [in Ukrainian].
Citations
1. Almaatouq A., Shmueli E., Nouh M., Alabdulkareem A., Singh V.K., Alsaleh M., Alarifi A., Alfaris A., Pentland A. If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts. International Journal of Information Security. 2016. Vol. 15, № 5. P. 475-491. URL: https://doi.org/10.1007/s10207-016-0321-5 (дата звернення: 10.03.2025).
2. Castillo C., Mendoza M., Poblete B. Information credibility on twitter. 20th international conference on World wide web, 2011. P. 675–684. URL: https://doi.org/10.1145/1963405.1963500 (дата звернення: 10.03.2025).
3. Pennycook G., Rand D.G. Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. Journal of Personality. 2020. Vol. 88(2). P.185–200. URL: https://doi.org/10.1111/jopy.12476 (дата звернення: 10.03.2025).
4. Karami M., Nazer T.H., Liu H. Profiling fake news spreaders on social media through psychological and motivational factors. 32nd ACM conference on hypertext and social media, 2021. P. 225–230. URL: https://doi.org/10.1145/3465336.347509 (дата звернення: 10.03.2025).
5. Shu K., Wang S., Liu H. Understanding user profiles on social media for fake news detection. 2018 IEEE conference on multimedia information processing and retrieval (MIPR), 2018. P. 430–435. DOI: 10.1109/MIPR.2018.00092 (дата звернення: 10.03.2025).
6. Shu K., Wang S., Liu H. Beyond news contents: the role of social context for fake news detection. Twelfth ACM international conference on web search and data mining, 2019. P. 312–320 URL: https://doi.org/10.1145/3289600.3290994 (дата звернення: 10.03.2025).
7. Rangel F., Giachanou A., Ghanem B.H.H., Rosso P. Overview of the 8th author profiling task at pan 2020: profiling fake news spreaders on Twitter. 11th International Conference of the CLEF Association, 2020. Vol. 2696. DOI: 10.1007/978-3-030-58219-7_25 (дата звернення: 10.03.2025).
8. Cardaioli M., Cecconello S., Conti M., Pajola L., Turrin F. Fake news spreaders profiling through behavioural analysis. Working Notes of Conference and Labs of the Evaluation Forum, 2020. URL: https://ceur-ws.org/Vol-2696/paper_113.pdf (дата звернення: 10.03.2025).
9. Pizarro J. Using n-grams to detect fake news spreaders on Twitter. Working Notes of Conference and Labs of the Evaluation Forum, 2020. URL: https://ceur-ws.org/Vol-2696/paper_181.pdf (дата звернення: 10.03.2025).
10. Vogel I., Meghana M. Fake news spreader detection on Twitter using character n-grams. Working Notes of Conference and Labs of the Evaluation Forum, 2020. URL: https://ceur-ws.org/Vol-2696/paper_59.pdf (дата звернення: 10.03.2025).
11. Giachanou A., Ríssola E.A., Ghanem B., Crestani F., Rosso P. The role of personality and linguistic patterns in discriminating between fake news spreaders and fact checkers. International conference on applications of natural language to information systems. Springer. 2020. P. 181–192. DOI: 10.1007/978-3-030-51310-8_17. (дата звернення: 10.03.2025).
12. Chen T., Li X., Yin H., Zhang J. Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection. Lecture Notes in Computer Science. 2018. Vol. 11154. P. 40-52. URL: https://doi.org/10.48550/arXiv.1704.05973 (дата звернення: 05.04.2025).
13. Bian T., Xiao X., Xu T., Zhao P., Huang W., Rong Y., Huang J. Rumor detection on social media with bi-directional graph convolutional networks. AAAI 2020. 2020. URL: https://doi.org/10.48550/arXiv.2001.06362 (дата звернення: 10.03.2025).
14. Lu Y., Li C. GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media. 58th Annual Meeting of the Association for Computational Linguistics, 2020. P. 505–514. URL: https://doi.org/10.48550/arXiv.2004.11648 (дата звернення: 05.04.2025).
15. Nguyen V., Sugiyama K., Nakov P., Kan M. FANG: Leveraging Social Context for Fake News Detection Using Graph Representation. 29th ACM International Conference on Information and Knowledge Management, 2020. P. 1165 – 1174. URL: https://doi.org/10.1145/3340531.3412046 (дата звернення: 15.03.2025).
16. Vosoughi S., Roy D., Aral S. The spread of true and false news online. Science. 2018. Vol. 359, No. 6380. P. 1146–1151. URL: https://www.science.org/doi/10.1126/science.aap9559(дата звернення: 15.03.2025).
17. Kim J., Kim D., Oh A. Homogeneity-based transmissive process to model true and false news in social networks. 12th ACM International Conference on Web Search and Data Mining, 2019. URL: https://doi.org/10.48550/arXiv.1811.09702 (дата звернення: 20.03.2025).
18. Rath B., Gao W., Srivastava J. Evaluating vulnerability to fake news in social networks: a community health assessment model. 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019. DOI: 10.1145/3341161.3342920 (дата звернення: 20.03.2025).
19. Лозинська О., Марків О., Висоцька В., Романчук Р., Назаркевич М. Інформаційна технологія розроблення та наповнення датасету дезінформації з використанням інтелектуального пошуку дипфейків та клікбейтів. Herald of Khmelnytskyi National University. Technical Sciences. 2024. № 343, т. 6(1). С. 158-167. URL: https://doi.org/10.31891/2307-5732-2024-343-6-24 (дата звернення: 20.03.2025).
Copyright (c) 2025 Olga Lozynska, Oksana Markiv, Victoria Vysotska
A Method for Detecting Disinformation Spreaders Based on a Graph Representation of the Social Network Structure
About the Authors
Olga Lozynska, Associate Professor, PhD in Information Technology (Candidate of Technical Sciences), Associate Professor of Information Systems and Networks Department, 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 Information Systems and Networks Department, Lviv Polytechnic National University, Lviv, Ukraine, ORCID:0000-0002-1691-1357, e-mail: oksana.o.markiv@lpnu.ua
Victoria Vysotska, Associate Professor, Doctor in Information Technology (Doctor of Technical Sciences), Professor of Information Systems and Networks Department, Lviv Polytechnic National University, Lviv, Ukraine, ORCID: 0000-0001-6417-3689, e-mail: victoria.a.vysotska@lpnu.ua
Abstract
Keywords
Full Text:
PDFReferences
1. Almaatouq, A., Shmueli, E., Nouh, M., Alabdulkareem, A., Singh, V.K., Alsaleh, M., Alarifi, A., Alfaris, A., & Pentland, A. (2016). If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts. International Journal of Information Security. 15 (5), 475-491. https://doi.org/10.1007/s10207-016-0321-5.
2. Castillo, C., Mendoza, M., & Poblete, B. (2011). Information credibility on Twitter. 20th international conference on World wide web (675–684). https://doi.org/10.1145/1963405.1963500.
3. Pennycook, G., & Rand, D.G. (2020). Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. Journal of Personality. 88(2), 185–200. https://doi.org/10.1111/jopy.12476.
4. Karami, M., Nazer, T.H., & Liu, H. (2021). Profiling fake news spreaders on social media through psychological and motivational factors. 32nd ACM conference on hypertext and social media (225–230). https://doi.org/10.1145/3465336.347509.
5. Shu, K., Wang, S., & Liu, H. (2018). Understanding user profiles on social media for fake news detection. 2018 IEEE conference on multimedia information processing and retrieval (430–435). DOI: 10.1109/MIPR.2018.00092.
6. Shu, K., Wang, S., & Liu, H. (2019). Beyond news contents: the role of social context for fake news detection. Twelfth ACM international conference on web search and data mining (312–320). URL: https://doi.org/10.1145/3289600.3290994.
7. Rangel, F., Giachanou, A., Ghanem, B.H.H., & Rosso, P. (2020). Overview of the 8th author profiling task at pan 2020: profiling fake news spreaders on Twitter. 11th International Conference of the CLEF Association. DOI: 10.1007/978-3-030-58219-7_25.
8. Cardaioli, M., Cecconello, S., Conti, M., Pajola L., & Turrin F. (2020). Fake news spreaders profiling through behavioural analysis. Working Notes of Conference and Labs of the Evaluation Forum. https://ceur-ws.org/Vol-2696/paper_113.pdf .
9. Pizarro, J. (2020). Using n-grams to detect fake news spreaders on Twitter. Working Notes of Conference and Labs of the Evaluation Forum. https://ceur-ws.org/Vol-2696/paper_181.pdf.
10. Vogel, I., & Meghana, M. (2020). Fake news spreader detection on Twitter using character n-grams. Working Notes of Conference and Labs of the Evaluation Forum. https://ceur-ws.org/Vol-2696/paper_59.pdf.
11. Giachanou, A., Ríssola, E.A., Ghanem, B., Crestani, F., & Rosso, P. (2020). The role of personality and linguistic patterns in discriminating between fake news spreaders and fact checkers. International conference on applications of natural language to information systems (181–192). DOI: 10.1007/978-3-030-51310-8_17.
12. Chen, T., Li, X., Yin, H., & Zhang, J. (2018). Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection. Lecture Notes in Computer Science. 11154, 40-52. URL: https://doi.org/10.48550/arXiv.1704.05973.
13. Bian, T., Xiao, X., Xu, T., Zhao, P., Huang, W., Rong, Y., & Huang, J. (2020). Rumor detection on social media with bi-directional graph convolutional networks. AAAI 2020. https://doi.org/10.48550/arXiv.2001.06362.
14. Lu, Y., & Li, C. (2020). GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media. 58th Annual Meeting of the Association for Computational Linguistics (505–514). https://doi.org/10.48550/arXiv.2004.11648 .
15. Nguyen, V., Sugiyama, K., Nakov, P., & Kan, M. (2020). FANG: Leveraging Social Context for Fake News Detection Using Graph Representation. 29th ACM International Conference on Information and Knowledge Management (1165 – 1174). https://doi.org/10.1145/3340531.3412046.
16. Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science. 359 (6380), 1146–1151. https://www.science.org/doi/10.1126/science.aap9559.
17. Kim, J., Kim, D., & Oh, A. (2019). Homogeneity-based transmissive process to model true and false news in social networks. 12th ACM International Conference on Web Search and Data Mining. https://doi.org/10.48550/arXiv.1811.09702.
18. Rath, B., Gao, W., & Srivastava, J. (2019). Evaluating vulnerability to fake news in social networks: a community health assessment model. 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). DOI: 10.1145/3341161.3342920.
19. Lozynska, O., Markiv, O., Vysotska, V., Romanchuk, R., & Nazarkevych, M. (2024). Information technology for developing and populating a disinformation dataset using intelligent deepfakes and clickbait search. Herald of Khmelnytskyi National University. Technical Sciences, 343(6(1)), 158-167. DOI: https://doi.org/10.31891/2307-5732-2024-343-6-24 [in Ukrainian].
Citations
1. Almaatouq A., Shmueli E., Nouh M., Alabdulkareem A., Singh V.K., Alsaleh M., Alarifi A., Alfaris A., Pentland A. If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts. International Journal of Information Security. 2016. Vol. 15, № 5. P. 475-491. URL: https://doi.org/10.1007/s10207-016-0321-5 (дата звернення: 10.03.2025).
2. Castillo C., Mendoza M., Poblete B. Information credibility on twitter. 20th international conference on World wide web, 2011. P. 675–684. URL: https://doi.org/10.1145/1963405.1963500 (дата звернення: 10.03.2025).
3. Pennycook G., Rand D.G. Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. Journal of Personality. 2020. Vol. 88(2). P.185–200. URL: https://doi.org/10.1111/jopy.12476 (дата звернення: 10.03.2025).
4. Karami M., Nazer T.H., Liu H. Profiling fake news spreaders on social media through psychological and motivational factors. 32nd ACM conference on hypertext and social media, 2021. P. 225–230. URL: https://doi.org/10.1145/3465336.347509 (дата звернення: 10.03.2025).
5. Shu K., Wang S., Liu H. Understanding user profiles on social media for fake news detection. 2018 IEEE conference on multimedia information processing and retrieval (MIPR), 2018. P. 430–435. DOI: 10.1109/MIPR.2018.00092 (дата звернення: 10.03.2025).
6. Shu K., Wang S., Liu H. Beyond news contents: the role of social context for fake news detection. Twelfth ACM international conference on web search and data mining, 2019. P. 312–320 URL: https://doi.org/10.1145/3289600.3290994 (дата звернення: 10.03.2025).
7. Rangel F., Giachanou A., Ghanem B.H.H., Rosso P. Overview of the 8th author profiling task at pan 2020: profiling fake news spreaders on Twitter. 11th International Conference of the CLEF Association, 2020. Vol. 2696. DOI: 10.1007/978-3-030-58219-7_25 (дата звернення: 10.03.2025).
8. Cardaioli M., Cecconello S., Conti M., Pajola L., Turrin F. Fake news spreaders profiling through behavioural analysis. Working Notes of Conference and Labs of the Evaluation Forum, 2020. URL: https://ceur-ws.org/Vol-2696/paper_113.pdf (дата звернення: 10.03.2025).
9. Pizarro J. Using n-grams to detect fake news spreaders on Twitter. Working Notes of Conference and Labs of the Evaluation Forum, 2020. URL: https://ceur-ws.org/Vol-2696/paper_181.pdf (дата звернення: 10.03.2025).
10. Vogel I., Meghana M. Fake news spreader detection on Twitter using character n-grams. Working Notes of Conference and Labs of the Evaluation Forum, 2020. URL: https://ceur-ws.org/Vol-2696/paper_59.pdf (дата звернення: 10.03.2025).
11. Giachanou A., Ríssola E.A., Ghanem B., Crestani F., Rosso P. The role of personality and linguistic patterns in discriminating between fake news spreaders and fact checkers. International conference on applications of natural language to information systems. Springer. 2020. P. 181–192. DOI: 10.1007/978-3-030-51310-8_17. (дата звернення: 10.03.2025).
12. Chen T., Li X., Yin H., Zhang J. Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection. Lecture Notes in Computer Science. 2018. Vol. 11154. P. 40-52. URL: https://doi.org/10.48550/arXiv.1704.05973 (дата звернення: 05.04.2025).
13. Bian T., Xiao X., Xu T., Zhao P., Huang W., Rong Y., Huang J. Rumor detection on social media with bi-directional graph convolutional networks. AAAI 2020. 2020. URL: https://doi.org/10.48550/arXiv.2001.06362 (дата звернення: 10.03.2025).
14. Lu Y., Li C. GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media. 58th Annual Meeting of the Association for Computational Linguistics, 2020. P. 505–514. URL: https://doi.org/10.48550/arXiv.2004.11648 (дата звернення: 05.04.2025).
15. Nguyen V., Sugiyama K., Nakov P., Kan M. FANG: Leveraging Social Context for Fake News Detection Using Graph Representation. 29th ACM International Conference on Information and Knowledge Management, 2020. P. 1165 – 1174. URL: https://doi.org/10.1145/3340531.3412046 (дата звернення: 15.03.2025).
16. Vosoughi S., Roy D., Aral S. The spread of true and false news online. Science. 2018. Vol. 359, No. 6380. P. 1146–1151. URL: https://www.science.org/doi/10.1126/science.aap9559(дата звернення: 15.03.2025).
17. Kim J., Kim D., Oh A. Homogeneity-based transmissive process to model true and false news in social networks. 12th ACM International Conference on Web Search and Data Mining, 2019. URL: https://doi.org/10.48550/arXiv.1811.09702 (дата звернення: 20.03.2025).
18. Rath B., Gao W., Srivastava J. Evaluating vulnerability to fake news in social networks: a community health assessment model. 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019. DOI: 10.1145/3341161.3342920 (дата звернення: 20.03.2025).
19. Лозинська О., Марків О., Висоцька В., Романчук Р., Назаркевич М. Інформаційна технологія розроблення та наповнення датасету дезінформації з використанням інтелектуального пошуку дипфейків та клікбейтів. Herald of Khmelnytskyi National University. Technical Sciences. 2024. № 343, т. 6(1). С. 158-167. URL: https://doi.org/10.31891/2307-5732-2024-343-6-24 (дата звернення: 20.03.2025).