Conceptual templates for creating fake news | Vestnik Tomskogo gosudarstvennogo universiteta – Tomsk State University Journal. 2025. № 515. DOI: 10.17223/15617793/515/12

Conceptual templates for creating fake news

The study aims to identify conceptual patterns of fake news creation and adapt them for use in machine learning systems to improve the accuracy of disinformation detection. A database of 387 fake news collected from various platforms, social networks and information resources was analyzed. The materials cover topics of politics, science, health and social issues, which made it possible to identify universal strategies for creating disinformation. The main research method was the adaptation of Jacob Goldenberg's "basic patterns of quality advertising" using the analogy method. This made it possible to identify universal principles actively used in the creation of fake news. Each of the identified patterns was tested for applicability and reliability. For this purpose, validation was carried out with the participation of 30 respondents, whose results showed a high level of consistency (Cronbach's alpha coefficient = 0.89). As a result of the study, 15 variations of conceptual templates for creating fake news were developed and described: 1. "Figurative analogy" - the use of visual semiotics to create unusual images ("Substitution" - one element of the situation is replaced by a visually similar symbol; "Simplification" - exaggeration or simplification of known analogies or a specific situation). 2. "Exaggerated situation" - hypertrophy of the properties of a situation or object. ("Absurdity" - demonstration of absurd decisions; "Accentuation" - exaggeration of the significance of an event). 3. "Consequences" - manipulation of fear of the future through isolated facts ("Common problems" - exaggeration of threats to society; "Inversion problems" - negative consequences of refusing to make decisions). 4. "Competition" -comparison of a situation with another, real or fictional ("Attributive competition" - demonstration of the weakness of an object in comparison with another; "Alternative competition" - comparison with hypothetical conditions). 5. "Interactive engagement" - encouraging the audience to act through interactive elements ("Appeal" - a direct appeal through emotional content; "Participation" - nudging to mental participation). 6. "Violation of logic" - manipulation through distortion of temporal, spatial and logical aspects ("Addition" - linking unrelated phenomena; "Multiplication" - exaggeration of the scale of the problem; "Division" - breaking an event into small parts to increase drama; "Anticipation" - transferring events to the past or future). 7. "Deception" - creation of completely fictitious content. The study showed that the proposed patterns are universal conceptual tools for analyzing and classifying fake news. Their integration into machine learning systems allows to increase the accuracy of disinformation detection. The presented results are of significant practical importance for the development of algorithms to combat fake news and can be used by specialists in the field of data analysis, information security and media. The authors declare no conflicts of interests.

Download file
Counter downloads: 3

Keywords

fake news, manipulative content, disinformation, media communications, language models

Authors

NameOrganizationE-mail
Tkhorikov Boris A.The Kosygin State University of Russiatkhorikov-ba@rguk.ru
Klimenko Viktor A.National Research Tomsk State Universityklimenko@siberia.design
Osadchaya Olga S.The Kosygin State University of Russiaosadchaya-os@rguk.ru
Matsepuro Daria M.National Research Tomsk State Universitydaria.matsepuro@mail.tsu.ru
Trufanov David A.National Research Tomsk State Universitydavid.trufanov@siberianai.tsu.ru
Всего: 5

References

Lazer D.M.J. et al. The science of fake news // Science. 2018. Vol. 359, № 6380. P. 1094-1096. doi: 10.1126/science.aao2998.
Vosoughi S., Roy D., Aral S. The spread of true and false news online // Science. 2018. Vol. 359, № 6380. P. 10.1126/science.aap9559.
Pennycook G., Rand D.G. Fighting misinformation on social media using crowdsourced judgments of news source quality // Proceedings of the National Academy of Sciences. 2019. Vol. 116, № 7. P. 2521-2526.
Shu K. et al. Fake news detection on social media: A data mining perspective // ACM SIGKDD explorations newsletter. 2017. Vol. 19. № 1. P. 2236. doi: 10.1145/3137597.3137600.
Ruiz J.M.G. Discerning disinformation through design: Exploring fake news website design patterns // The Asian Conference on Media, Communication & Film. 2018. URL: https://papers.iafor.org/submission42587/.
Wang W.Y., Chang Y.C., Peng W.C. Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection // arXiv preprint arXiv:2401.15509. 2024.
Tandoc Jr E.C., Lim Z.W., Ling R. Defining «fake news» A typology of scholarly definitions // Digital journalism. 2018. Vol. 6, № 2. P. 137-153.
Pennycook G., Rand D.G. Fighting misinformation on social media using crowdsourced judgments of news source quality // Proceedings of the National Academy of Sciences. 2019. Vol. 116, № 7. P. 2521-2526.
Wu J. et al. Prompt-and-align: prompt-based social alignment for few-shot fake news detection // Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2023. P. 2726-2736.
Guo H. et al. Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection // arXiv preprint arXiv:2412.14686. 2024.
Jin R. et al. Fake News Detection and Manipulation Reasoning via Large Vision-Language Models // arXiv preprint arXiv:2407.02042. 2024.
Hashmi E. et al. Advancing Fake News Detection: Hybrid Deep Learning With Fast Text and Explainable AI. // IEEE Access. 2024. Vol. 12. P. 44462-44480. doi: 10.1109/access.2024.3381038.
Dhawan M. et al. Game-on: Graph attention network based multimodal fusion for fake news detection // Social Network Analysis and Mining. 2024. Vol. 14. Art. No. 114. doi: 10.1007/s13278-024-01271-4.
Giri M., Eswaran S., Honnavalli P. Automated and Interpretable Fake News Detection With Explainable Artificial Intelligence // Journal of Applied Security Research. 2024. Vol. 19, Is. 4. P. 628-648. doi: 10.1080/19361610.2024.2356431.
Goldenberg J., Mazursky D., Solomon S. The fundamental templates of quality ads // Marketing science. 1999. Vol. 18, № 3. P. 333-351. doi: 10.1287/mksc.18.3.333.
Devlin J., Chang M.W., Lee K., Toutanova K. Bert: pre-training of deep bidirectional transformers for language understanding. 2018. arXiv preprint arXiv:1810.04805.
 Conceptual templates for creating fake news | Vestnik Tomskogo gosudarstvennogo universiteta – Tomsk State University Journal. 2025. № 515. DOI: 10.17223/15617793/515/12

Conceptual templates for creating fake news | Vestnik Tomskogo gosudarstvennogo universiteta – Tomsk State University Journal. 2025. № 515. DOI: 10.17223/15617793/515/12

Download full-text version
Counter downloads: 66