Mathematical Model of a Social Network User Profile Based on Interval Data Analysis
Keywords:
structural identification, parametric identification, artificial bee colony, social network user profileAbstract
It is proposed and substantiated to use a mathematical model for making decisions regarding the credibility of content posted on social networks, based on establishing a relationship between the outcome on which the decision about the credibility or unreliability of the content is made and the factors influencing it. Quantitative factors for assessing the user profile in the network have been justified: the number of posts, shares, or likes made by users within a short time after the content appears; the number of comments or reactions at certain time intervals; the number of participants interacting with the content within half a day after publication; the viral spread coefficient of the content. The resulting indicator of such a model is proposed to be the degree of credibility of specific content, ranging from 0 to 1. It is proposed and justified that methods of interval data analysis should be used to represent and analyze this indicator based on expert assessment of the content. An optimization problem is formulated for the two-stage identification of the model based on interval data analysis: forming the current structure based on candidate models (model structure synthesis), estimating its parameters and verifying the adequacy of the model. A hybrid method for identifying interval models of user profiles in a social network is proposed and substantiated. This method combines a metaheuristic algorithm for synthesizing the model structure based on the behavioral model of a bee colony with gradient methods for identifying the parameters of candidate models. Examples of applying the proposed method and mathematical model for making decisions about the credibility of content are presented.
References
O.S. Ulichev, "Research on models of information dissemination and informational influence in social networks," Systems of Control, Navigation and Communication, issue 4, pp. 147–151, 2018. [Online]. Available at: http://nbuv.gov.ua/UJRN/suntz_2018_4_31. (in Ukrainian)
Ye. Ivohin, L. Adzhubey, "On modeling the dynamics of information dissemination based on heterogeneous diffusion hybrid models," Scientific Bulletin of Uzhhorod University. Series: Mathematics and Computer Science, pp. 112–118, 2019. https://doi.org/10.24144/2616-7700.2019.2(35).112-118. (in Ukrainian)
O. Ulichev, Ye. Meleshko, D. Sawicki, S. Smailova, "Computer modeling of dissemination of informational influences in social networks with different strategies of information distributors," Proc. SPIE 11176, Wilga, Poland, 2019, Article No.: 111761T. https://doi.org/10.1117/12.2536480. (in Ukrainian)
S. Vosoughi, D. Roy, and S. Aral, "The spread of true and false news online," Science, vol. 359, no. 6380, pp. 1146–1151, 2018. https://doi.org/10.1126/science.aap9559.
M. Del Vicario, A. Bessi, F. Zollo, F. Petroni, A. Scala, G. Caldarelli, H. E. Stanley, and W. Quattrociocchi, "The spreading of misinformation online," Proceedings of the National Academy of Sciences, vol. 113, no. 3, pp. 554–559, 2016. https://doi.org/10.1073/pnas.1517441113.
Y. Wang, F. Ma, Z. Jin, Y. Yuan, G. Xun, L. Jiao, and A. Su, "EANN: Event adversarial neural networks for multi-modal fake news detection," Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 849–857, 2018. https://doi.org/10.1145/3219819.3219903.
J. Ma, W. Gao, P. Mitra, S. Kwon, B. J. Jansen, K. Wong, and M. Cha, "Detecting rumors from microblogs with recurrent neural networks," Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 3818–3824, 2016.
K. Shu, D. Mahudeswaran, S. Wang, D. Lee, and H. Liu, "Hierarchical propagation networks for fake news detection: Investigation and exploitation," Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 626–637, 2020. https://doi.org/10.1609/icwsm.v14i1.7329.
J. Zhang, B. Dong, and S. Y. Philip, "FakeDetector: Effective fake news detection with deep diffusive neural network," 2020 IEEE 36th International Conference on Data Engineering, pp. 1826–1829, 2020. https://doi.org/10.1109/ICDE48307.2020.00180.
M. R. Islam, S. Liu, X. Wang, and G. Xu, "Deep learning for misinformation detection on online social networks: A survey and new perspectives," Social Network Analysis and Mining, vol. 10, no. 1, pp. 1–20, 2020. https://doi.org/10.1007/s13278-020-00696-x.
K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, "Fake news detection on social media: A data mining perspective," ACM SIGKDD Explorations Newsletter, vol. 19, no. 1, pp. 22–36, 2017. https://doi.org/10.1145/3137597.3137600.
M. Granik and V. Mesyura, "Fake news detection using naive Bayes classifier," 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering, pp. 900–903, 2017. https://doi.org/10.1109/UKRCON.2017.8100379.
S. P. Shary, "Interval methods for data fitting under uncertainty," Journal of Computational and Applied Mathematics, vol. 418, pp. 114–135, 2023. doi: 10.1016/j.cam.2022.114135.
O. Ulichev, Y. Meleshko, V. Khokh, "The computer simulation method of a social network structure for the research of dissemination processes of informational influences," Scientific and Practical Cyber Security Journal (SPCSJ), 4(3). – Georgia, Tbilisi, 2019, pp. 34–47 (in Ukrainian)
O.S. Ulichev, Ye.V. Meleshko, "Software modeling of the dissemination of informational and psychological influences in virtual social networks," Collection of Scientific Papers 'Modern Information Systems', Issue 2(2). – Kharkiv: NTU KhPI, 2018, pp. 35–39. https://doi.org/10.20998/2522-9052.2018.2.06 (in Ukrainian)
A. Guess, J. Nagler, and J. Tucker, "Less than you think: Prevalence and predictors of fake news dissemination on Facebook," Science Advances, vol. 5, no. 1, pp. 1–8, 2019. https://doi.org/10.1126/sciadv.aau4586.
K. Shu, D. Mahudeswaran, S. Wang, D. Lee, and H. Liu, "FakeNewsNet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media," Big Data, vol. 8, no. 3, pp. 171–188, 2020. https://doi.org/10.1089/big.2020.0062.
M. Zubiaga, A. Aker, K. Bontcheva, M. Liakata, and R. Procter, "Detection and resolution of rumours in social media: A survey," ACM Computing Surveys, vol. 51, no. 2, pp. 1–36, 2018. https://doi.org/10.1145/3161603.
M. J. Metzger, A. J. Flanagin, and R. B. Medders, "Social and heuristic approaches to credibility evaluation online," Journal of Communication, vol. 60, no. 3, pp. 413–439, 2010. https://doi.org/10.1111/j.1460-2466.2010.01488.x.
L. Zhou, D. Zhang, and C. C. Lee, "A survey of opinion mining and sentiment analysis," Mining Text Data, pp. 415–463, 2012. https://doi.org/10.1007/978-1-4614-3223-4_13.
K. Shu, S. Wang, and H. Liu, "Beyond news contents: The role of social context for fake news detection," Proceedings of the 12th ACM International Conference on Web Search and Data Mining, pp. 312–320, 2019. https://doi.org/10.1145/3289600.3290994.
H. Karimi and J. Tang, "Learning hierarchical discourse-level structure for fake news detection," Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, pp. 3432–3442, 2019. https://doi.org/10.18653/v1/N19-1347.
O.S. Ulichev, Ye.V. Meleshko, "Modeling of dissemination and neutralization processes of informational influences in a segment of a social network," Scientific Journal 'Information Protection'. – Kyiv: NAU, 2020, pp. 166–176 (in Ukrainian)
K. Mateńczuk, A. Kozina, A. Markowska, K. Czerniachowska, K. Kaczmarczyk, P. Golec, M. Hernes, K. Lutosławski, A. Kozierkiewicz, M. Pietranik, A. Rot, M. Dyvak, "Financial Time Series Forecasting: Comparison of Traditional and Spiking Neural Networks," Procedia Computer Science, vol. 192, 2021, pp. 5023-5029, https://doi.org/10.1016/j.procs.2021.09.280.
A. K. Jain and B. B. Gupta, "A machine learning based approach for phishing detection using hyperlinks information," Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 5, pp. 2015–2028, 2019. https://doi.org/10.1007/s12652-018-0798-z.
M. Anderson, P. Wilson, "Interval arithmetic in optimization: Theory and applications," Applied Mathematics and Computation, vol. 456, 2023, pp. 1–18.
M. Dyvak, I. Spivak, A. Melnyk, V. Manzhula, T. Dyvak, A. Rot, M. Hernes, "Modeling Based on the Analysis of Interval Data of Atmospheric Air Pollution Processes with Nitrogen Dioxide due to the Spread of Vehicle Exhaust Gases," Sustainability, vol. 15, 2023, p. 2163. https://doi.org/10.3390/su15032163.
I. Darmorost, M. Dyvak, N. Porplytsya, T. Shynkaryk, Y. Martsenyuk, V. Brych, "Convergence Estimation of a Structure Identification Method for Discrete Interval Models of Atmospheric Pollution by Nitrogen Dioxide," Proceedings of the 2019 9th International Conference on Advanced Computer Information Technologies (ACIT), Ceske Budejovice, Czech Republic, 2019, pp. 117–120. https://doi.org/10.1109/ACITT.2019.8779981.
M. Dyvak, "Parameters Identification Method of Interval Discrete Dynamic Models of Air Pollution Based on Artificial Bee Colony Algorithm," Proceedings of the 2020 10th International Conference on Advanced Computer Information Technologies (ACIT), Deggendorf, Germany, 2020, pp. 130–135. https://doi.org/10.1109/ACIT49673.2020.9208972.
D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Technical report, Erciyes University, Engineering Faculty, Computer Engineering Department, Erciyes University, 2005, 10 p. [Online]. Available at: https://abc.erciyes.edu.tr/pub/tr06_2005.pdf.
M. Karaboga, B. Akay, and D. Karaboga, "Artificial bee colony algorithm for optimization problems: A comprehensive review," Applied Soft Computing, vol. 122, 2022, pp. 108–125. doi: 10.1016/j.asoc.2022.108125.
J. Li, Y. Wang, and H. Chen, "Enhanced artificial bee colony algorithm with adaptive parameter control for global optimization," IEEE Transactions on Cybernetics, vol. 52, no. 8, 2022, pp. 7896–7908. doi: 10.1109/TCYB.2021.3082345
R. Sharma, S. Kumar, and P. K. Singh, "Artificial bee colony algorithm for feature selection in machine learning: A systematic review," Expert Systems with Applications, vol. 204, 2022, pp. 117–135. doi: 10.1016/j.eswa.2022.117135
L. Zhang, M. Wang, and X. Liu, "Multi-objective artificial bee colony algorithm for interval optimization problems," Information Sciences, vol. 625, 2023, pp. 1–18. https://doi.org/10.1016/j.ins.2023.01.045.
A. Kumar, D. Kumar, "A comprehensive review of artificial bee colony algorithm variants," Swarm and Evolutionary Computation, vol. 44, 2019, pp. 1–15.
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
