A model of the space of thematic telegram channels based on contextual links

Authors

  • Oleksandr Puchkov Institute of special communication and information protection at the National technical university of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine https://orcid.org/0000-0002-8585-1044
  • Dmytro Lande Educational and scientific physico-technical institute at the National technical university of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine https://orcid.org/0000-0003-3945-1178
  • Ihor Subach Institute of special communication and information protection at the National technical university of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine https://orcid.org/0000-0002-9344-713X

DOI:

https://doi.org/10.20535/2411-1031.2024.12.2.315731

Keywords:

Telegram channels, contextual links, information space, cybersecurity, network models, citation, communication interaction, content monitoring, CyberAggregator system

Abstract

The paper analyzes the existing models for describing the topology of the news web space, which reflect its division into coherent components such as its central part and peripheral areas, and proposes a new network model of thematic Telegram channels based on the idea of assessing the level of citation of individual information channels and taking into account direct links in messages from Telegram channels. It combines the content aspect of messages with the ability to take into account quantitative parameters. The study focuses on channels dedicated to cybersecurity and covers the first quarter of 2024. Based on the analysis of about 3,500 Telegram channels, more than 1,000 hyperlinked channels were identified and key areas of the information space, such as the communication zone, the communication core, and incoming and outgoing source segments, were outlined. The formed network is defined as a scale-free, structural network with self-similar properties and a power law distribution of node degrees, which confirms the applicability of the Pareto law to describe this space. A mathematical model is proposed that allows estimating the polynomial relationship between the volume of the communication area and the total number of sources. On the example of the CyberAggregator content monitoring system for information sources, a methodology for automated expansion of the database of target sources in the system is proposed, which provides dynamic enrichment of the list of information resources through the analysis of new contextual references in messages and an algorithm for its implementation.

Author Biographies

Oleksandr Puchkov, Institute of special communication and information protection at the National technical university of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv

PhD in philosophy, professor, head

Dmytro Lande, Educational and scientific physico-technical institute at the National technical university of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv

doctor of technical sciences, professor, chair of the academic department of the information security

Ihor Subach, Institute of special communication and information protection at the National technical university of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv

doctor of technical sciences, professor, chair of the academic department of the cyber security and application of information systems and technologies

References

D. Lande, I. Subach, and O. Puchkov, “System of Analysis of Big Data from Social Media, Information & Security: An International Journal, no. 47 (1), pp. 44-61, 2020. doi: https://doi.org/10.11610/isij.4703.

D. Lande, I. Subach, and A. Gladun, Processing of ultra-large data arrays (Big Data): a textbook. Kyiv, Ukraine: LTD “Engineering”, 2021.

D. Boyd, and K. Crawford, “Critical questions for Big Data”, Journal Information, Communication & Society, vol. 15, iss. 5, pp. 662-679, 2012, doi: https://doi.org/10.1080/1369118X.2012.678878.

A. Broder, R. Kumar, F. Maghoul, et al., “Graph structure in the Web”, Computer Networks. vol. 33, iss. 1-6, 2000, pp. 309-320. doi: https://doi.org/10.1016/S1389–1286(00)00083-9.

R. Meusel, S. Vigna, O. Lehmberg, and C. Bizer, “Graph structure in the web – revisited: a trick of the heavy tail”, in Proc. 23rd International Conference on World Wide Web (WWW'14 Companion), Seoul, Korea, 2014, pp. 427-432. doi: https://doi.org/10.1145/2567948.2576928.

O. Dodonov, D. Lande, and V. Putiatyn, Information flows in global computer networks. Kyiv, Ukraine: Naukova dumka, 2009.

A. Snarskyi, D. Lande, and I. Subach, Fundamentals of the theory of complex networks: textbook. Kyiv, Ukraine: LTD “Engineering”, 2023.

A. Réka, and A.L. Barabási, “Statistical mechanics of complex networks”, Reviews of Modern Physics, no. 74 (47), pp. 47-97, 2002. doi: https://doi.org/10.1103/RevModPhys.74.47.

A. Réka, J. Hawoong, and A.L. Barabási, “Error and attack tolerance of complex networks”, Nature, vol. 406, pp. 378-382, 2000. [Online]. Available: https://www.nature.com/articles/35019019. Accessed on: July 19, 2024.

A.L. Barabási, and A. Réka, “Emergence of scaling in random networks”, Science, vol. 286, iss. 5439, pp. 509-512, 1999. doi: https://doi.org/10.1126/science.286.5439.509.

G. Szabo, G. Polatkan, P.O. Boykin, and A. Chalkiopoulos, Social Media Data Mining and Analytics. Chichester, England: John Wiley & Sons Inc., 2018.

K. Cherven, Mastering Gephi Network Visualization. Birmingham, England: Packt Publishing, 2015.

I.V. Bezsudnov, and A.A. Snarskii, “From the time series to the complex networks: The parametric natural visibility graph”, Physica A: Statistical Mechanics and its Applications, vol. 414, pp. 53-60. doi: https://doi.org/10.1016/j.physa.2014.07.002.

L. Lacasa, B. Luque, F. Ballesteros, J. Luque, and J.C. Nuno, “From time series to complex networks: The visibility graph”, Proceedings of the National Academy of Sciences, vol. 105 (13), pp. 4972-4975, 2008. doi: https://doi.org/10.1073/pnas.0709247105.

D. Lande, E. Shnurko-Tabakova, “OSINT as a part of cyber defense system”, Theoretical and Applied Cybersecurity, no. 1, pp. 103108, 2019. [Online]. Available: http://tacs.ipt.kpi.ua/article/view/169091/168863. Accessed on: July 10, 2024.

Published

2024-12-26

How to Cite

Puchkov, O., Lande, D., & Subach, I. (2024). A model of the space of thematic telegram channels based on contextual links. Collection "Information Technology and Security", 12(2), 151–161. https://doi.org/10.20535/2411-1031.2024.12.2.315731

Issue

Section

INFORMATION TECHNOLOGY