Determining the interests of Social Network Users

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Author(s)

Irada Y. Alakbarova 1,*

1. Institute of Information Technologies, Baku, Azerbaijan

* Corresponding author.

DOI: https://doi.org/10.5815/ijeme.2023.04.01

Received: 30 Dec. 2022 / Revised: 13 Feb. 2023 / Accepted: 3 Mar. 2023 / Published: 8 Aug. 2023

Index Terms

Social Networks, Big Social Data, Data Mining, Machine Learning, Naive Bayes Classifier, Ranking of Actors

Abstract

The article is devoted to a brief review of approaches to the analysis of social relations in social networks using comments and credentials located in the profiles of social network users. The study aims to determine the interest and behavior of each user. The approach that we propose to determine the interests of social network users requires some methods of machine learning (classification analysis and data clustering). A method based on sentiment analysis and a naive Bayesian classifier is proposed. Determining the interests of social network users based on the intellectual analysis of comments can help to understand the logic of their behavior, and determine social relations between users and problems in society.

Cite This Paper

Irada Alakbarova, "Determining the interests of Social Network Users", International Journal of Education and Management Engineering (IJEME), Vol.13, No.4, pp. 1-8, 2023. DOI:10.5815/ijeme.2023.04.01

Reference

[1]F.N. Van der Vlist, A. Helmond, M. Burkhardt, T. Seitz. API Governance: The Case of Facebook’s Evolution, Social Media + Societ, 2022, pp. 1–24, doi: 10.1177/20563051221086228.
[2]L. Massari. Analysis of MySpace user profiles, Information Systems Frontiers, 2010, vol. 12, no.4. pp. 361–367, doi: 10.1007/s10796-009-9206-8.
[3]I.H. Sarker. Machine Learning: Algorithms, Real-World Applications and Research Directions, SN Computer Science, 2021, vol. 2, no.160, pp. 1–21, doi: 10.1007/s42979-021-00592-x
[4]S.M. Dol, P.M. Jawandhiya Classification Technique and its Combination with Clustering and Association Rule Mining in Educational Data Mining — A survey, Engineering Applications of Artificial Intelligence, 2023, vol. 122, pp. 196–211, doi: 10.1016/j.engappai.2023.106071
[5]D. Xhemali, Ch.J. Hinde, R.G. Stone. Naïve Bayes vs. Decision Trees vs. Neural Networks in the classification of training web pages, Computer Science, 2009, vol. 4, no. 1, pp. 16–23.
[6]Liu D.W., Zhang Z.L., Guo X.H. Web mining based on one-dimensional Kohonen's algorithm: analysis of social media websites, Neural Computing & Applications, 2017, vol. 28, pp. S641–S645.
[7]T. I-Hsien Web mining techniques for on-line social networks analysis, Proceedings of the International Conference on Service Systems and Service Management, 30 June 2008 - 02 July 2008, doi: 10.1109/ICSSSM.2008.4598506
[8]M.M. Akhtar, A.S. Zamani, A. El-Sayed. Link Analysis using Data Mining System, Applied Research in Computer Science and Information Technology, 2012, vol. 1, no. 2, pp. 38–49.
[9]R.M. Alguliev, R.M. Aliguliyev, I.Ya Alakbarova. Cluster approach to the efficient use of multimedia resources in information warfare in Wikimedia, Automatic Control and Computer Sciences, 2014, vol. 48, no. 2, pp. 97–108.
[10]R.M. Alguliyev, R.M. Aliguliyev, G.Y. Niftaliyeva. Filtration of Terrorism Related Texts in the E-government Environment. International, Journal of Cyber Warfare and Terrorism, 2018. vol. 8, issue 4, pp. 35–48.
[11]T. Pikulík, P. Štarchoň Public registers with personal data under scrutiny of DPA regulators, Procedia Computer Science, 2020, vol. 170, pp. 1174–1179.
[12]J. Rabelo, R.B.C. Prudêncio, F. Barros Collective Classification for Sentiment Analysis in Social Networks, Proceedings of the 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, 7-9 Nov, 2012, , vol.1. doi: 10.1109/ICTAI.2012.135
[13]N.B. Seghouani, C.N. Jipmo, G. Quercini. Determining the interests of social media users: two approaches, Information Retrieval Journal, 2019, vol. 22, pp. 129–158.
[14]Shchepinaa E., Surikova A. Modeling the trajectories of interests and preferences of users in digital social systems, Proceedings of the 11th International Young Scientist Conference on Computational Science, Procedia Computer Science 212, 2022, pp. 104–113.
[15]L. Getoor Link mining: A new data mining challenge, ACM SIGKDD Explorations Newsletter, 2003, vol. 5, Issue 1, pp. 84–89, doi: 10.1145/959242.959253
[16]E. Olshannikova, T. Olsson, J. Huhtamäki, H. Kärkkäinen. Conceptualizing Big Social Data, Journal of Big Data, 2017, vol. 4, no. 3, doi: 10.1186/с40537-017-0063-х.
[17]A. Mtibaa, M. May, C. Diot, M. Ammar. PeopleRank: Social Opportunistic Forwarding, Proceedings of the IEEE INFOCOM, 14–19 March, 2010, San Diego, USA, 2010, pp. 1–5.
[18]W. Tan, M.W. Blake, I. Saleh, S. Dustdar. Social-network-sourced big data analytics, IEEE Internet Computing, 2013, vol.17, no.5, pp.62–69, doi: 10.1109/MIC.2013.100
[19]A. Perer, I. Guy, E. Uziel, I. Ronen, M. Jacovi, Visual social network analytics for relationship discovery in the enterprise, Proceedings of the IEEE Conference on Visual Analytics Science and Technology, Providence, RI, USA, 23–28 October 2011, pp. 71–79.
[20]G. Bello-Orgaz, J.J. Jung, D. Camacho, Social big data: Recent achievements and new challenges, Information Fusion, 2016, vol. 28, pp. 45–59, doi: 10.1016/j.inffus.2015.08.005
[21]M. Taboada, J. Brooke, M. Tofiloski, K. Voll, M. Stede, Lexicon-Based Methods for Sentiment Analysis, Computational Linguistics, 2011, 37 (2), pp. 267–307, doi: 10.1162/COLI_a_00049
[22]N. Li, Q. Huang, X. Ge, Collective Behavior Analysis and Graph Mining in Social Networks, Complexity, 2022, Special Issue, Article ID 6692210.
[23]A. Perer, I. Guy, E. Uziel, I. Ronen, M. Jacovi, Visual social network analytics for relationship discovery in the enterprise, Proceedings of the IEEE Conference on Visual Analytics Science and Technology, Providence, RI, USA, 23–28 October 2011. pp. 71–79.
[24]L. Zhang, Z. Cai, J. Lu, X. Wang, Mobility-Aware Routing in Delay Tolerant Networks, Personal and Ubiquitous Computing, 2015, vol. 19, no. 7. pp. 1111–1123, doi: 10.1007/s00779-015-0880-х
[25]J. Yang, X. Xiu, L. Sun, L. Ying, B. Muthu. Social media data analytics for business decision making system to competitive analysis, Information Processing &Managemen, 2022, vol. 59, Issue 1. 102751, https://doi.org/10.1016/j.ipm.2021.102751
[26]Ch. Mani. How Is Big Data Analytics Using Machine Learning?, Forbes, 2020, https://www.forbes.com/sites/forbestechcouncil/2020/10/20/how-is-big-data-analytics-using-machine-learning/?sh=5454cca571d2