Dynamic system of functioning of social network communities

E. P. Okhapkina ; V. P. Okhapkin ; R. V. Mescheriakov ; A. O. Iskhakova ; A. Yu. Iskhakov

News of the Kabardin-Balkar scientific center of RAS, no. 2 (2022), pp. 41-71.

Résumé

Social networks are no longer used only as a tool for global communication of various segments of society in different countries. It is turned into a socio-political asset in the struggle for the specific interests of a group of people who can acquire and/or manage this asset. In the paper we use data of a number of communities of the Russian-Ukrainian segment of the social network «Vkontakte». The study formalizes one of the functional features of a social network: a community (group). The community is considered from the position of a tool for forming opinions and aggressive influence on a single person, some small or wide community. The issue of using information and communication technologies in a destructive way is being updated. The processes occurring in the social network community are shown in the form of a system of first-order differential equations. The system is investigated for stability by the method of Lyapunov functions. One of the tasks of the study is to identify and characterize the border regimes in which the functioning of the community goes from a stable state to chaos. The simulation model of the constructed dynamic system under different initial simulation conditions is considered. The use of mathematical physics tools to describe the processes of cyber-physical systems, including in the task of evaluating text messages with signs of aggression, in a distributed computing environment allows us to assess the trajectory of their evolvement under various initial conditions.

Détail

Keywords: differential equations, social network services, system dynamics, stability analysis, simulation modeling
Mots-clés : information and communication technologies, dynamic equilib.

Bibliographie

[1] M. A. Tocoglu, O. Ozturkmenoglu, A. Alpkocak, “Emotion Analysis From Turkish Tweets Using Deep Neural Networks”, IEEE Access, 7 (2019), 183061–183069 | DOI

[2] A. Kumar, V. T. Narapareddy, V. Aditya Srikanth et al., “Sarcasm Detection Using MultiHead Attention Based Bidirectional LSTM”, IEEE Access, 8 (2020), 6388–6397 | DOI

[3] Y. Dong, Y. Fu, Wang L et al., “A Sentiment Analysis Method of Capsule Network Based on BiLSTM”, IEEE Access, 8 (2020), 37014–37020 | DOI

[4] H. Liang, U. Ganeshbabu, T. Thorne, “A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution”, IEEE Access, 8 (2020), 54164–54174 | DOI

[5] A. Kumar, V. T. Narapareddy, V. Aditya Srikanth et al, “Aspect-Based Sentiment Classification Using Interactive Gated Convolutional Network”, IEEE Access, 8 (2020), 22445–22453 | DOI

[6] W. Ding, “SVM-Based Feature Selection for Differential Space Fusion and Its Application to Diabetic Fundus Image Classification”, IEEE Access, 7 (2019), 149493–149502 | DOI

[7] M. Li, H. Wu, H. Zhang, “Matrix Factorization for Personalized Recommendation with Implicit Feedback and Temporal Information in Social Ecommerce Networks”, IEEE Access, 7 (2019), 141268–141276 | DOI

[8] J. Xu, Z. Xu, J. Chen, “Semantic retrieval system based on ontology”, Proceedings of the 5th WSEAS International Conference on Information Security and Privacy, World Scientific and Engineering Academy and Society, Stevens Point, Wisconsin, USA, 2006, 124–129

[9] Y. Zhao, S. Pan, J. Wu et al., “IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing”, IEEE Access, 8 (2020), 228598–228604 | DOI

[10] N. Rush, P. Abets, M. Lalua, Pryamoi metod Lyapunova v teorii ustoichivosti, Mir, Moskva, 1980, 300 pp. | MR

[11] S. A. Tsimfer, “Otsenka parametrov perekhodnogo protsessa lineinoi sistemy na osnove pryamogo metoda Lyapunova”, Protsessy upravleniya i ustoichivost, 3:1 (2016), 138–143

[12] B. S. Kalitine, “On solving the problems of stability by Lyapunov's direct method”, Russian Mathematics, 61:6 (2017), 27–36 | DOI | MR | Zbl

[13] D. R. Gulyaeva, A. V. Kiselev, “Primenenie metodov Lyapunova dlya issledovaniya ustoichivosti sistem”, Informatsionnye sistemy i tekhnologii, materialy dokladov II mezhdunarodnoi nauchno-tekhnicheskoi zaochnoi konferentsii «IST-2016», Yugo-Zapadnyi gosudarstvennyi universitet, Kursk, 2016, 40–44

[14] T. Yu. Plyusnina, P. V. Fursova, A. N. Dyakonova i dr., Matematicheskie modeli v biologii, uchebnoe posobie, NITs: Regulyarnaya i khaoticheskaya dinamika, M.–Izhevsk, 2021, 174 pp.

[15] I. V. Schurov, Obyknovennye differentsialnye uravneniya, Interaktivnyi uchebnik, (data obrascheniya: 25.09.2020) https://ode.mathbook.info/

[16] F. P. Vasilev, Chislennye metody resheniya ekstremalnykh zadach, Nauka, Moskva, 1988, 552 pp. | MR

[17] I. M. Yakimov, A. P. Kirpichnikov, R. D. Ustinov i dr, “Imitatsionnoe modelirovanie v sisteme strukturnogo i imitatsionnogo modelirovaniya Ithink-”, Vestnik Tekhnologicheskogo universiteta, 22:2 (2019), 159–164

[18] V. A. Minaev, M. P. Sychev, L. S. Kulikov i dr, “Modelirovanie manipulyativnykh vozdeistvii v sotsialnykh setyakh”, Modelirovanie, optimizatsiya i informatsionnye tekhnologii, 7:1 (24) (2019), 494–510 | DOI

[19] V. A. Minaev, S. V. Dvoryankin, “Obosnovanie i opisanie modeli dinamiki informatsionno-psikhologicheskikh vozdeistvii destruktivnogo kharaktera v sotsialnykh setyakh”, Bezopasnost informatsionnykh tekhnologii, 23:3 (2016), 40–52

[20] X. Cheng, S. Fu, G. J. de Vreede, “Understanding trust influencing factors in social media communication: A qualitative study”, International Journal of Information Management, 37:2 (2017), 25–35 | DOI