General algorithm for searching user data in social media of the Internet
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Submitted Dec 29, 2019
Published Jan 16, 2020
Published Jan 16, 2020
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Articles
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The research work presented within this paper solves the problem of automated search for heterogeneous data in social media of the Internet (SMI). Building a system for obtaining and subsequent analysis of heterogeneous data in SMI – a complex multi-stage process in which specialists of various profiles and qualifications participate. Therefore, one of the main problems in the design of such systems is the coverage of all aspects of the functioning of the software-analytical complex, providing a common language for specialists, which allows us to uniquely, and clearly, understandably formulate the basic concepts of the projects. One of the main and basic tasks in analyzing the pages of a SMI user is to build algorithms for analyzing the user data environment (UDE). The quality of software will depend on the implemented algorithms. The construction of such algorithms, on the one hand, provides an understanding of the process of forming functional individual modules of the system and their interaction, on the other hand, laying a qualitative foundation in the future system. Algorithms for data analysis in the SMI will be designed based on the basic principles of behavior of the user registered in it.
Keywords: Data Search, Social Environment of the Internet, Database, Data Validation, User Data Environment
References
Kim, Jason Z., et al. Role of graph architecture in controlling dynamical networks with applications to neural systems. Nature physics, 2018, 14.1: 91.
Nowland, Rebecca; Necka, Elizabeth A.; Cacioppo, John T. Loneliness and social internet use: pathways to reconnection in a digital world?. Perspectives on Psychological Science, 2018, 13.1: 70-87.
Nie, Zaiqing, et al. Automated social networking graph mining and visualization. U.S. Patent No 9,990,429, 2018.
Chang, Yung-Chun; Ku, Chih-Hao; Chen, Chun-Hung. Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor. International Journal of Information Management, 2019, 48: 263-279.
Etter, Michael, et al. Measuring organizational legitimacy in social media: Assessing citizens’ judgments with sentiment analysis. Business & Society, 2018, 57.1: 60-97.
Martínez-Castaño, Rodrigo, et al. A Micromodule Approach for Building Real-Time Systems with Python-Based Models: Application to Early Risk Detection of Depression on Social Media. In: European Conference on Information Retrieval. Springer, Cham, 2018. p. 801-805.
Fletcher, Richard; Nielsen, Rasmus Kleis. Are people incidentally exposed to news on social media? A comparative analysis. New media & society, 2018, 20.7: 2450-2468.
Bilecen, Başak; Gamper, Markus; Lubbers, Miranda J. The missing link: Social network analysis in migration and transnationalism. Social Networks, 2018, 53: 1-3.
Farine, Damien R. When to choose dynamic vs. static social network analysis. Journal of animal ecology, 2018, 87.1: 128-138.
Borgatti, Stephen P.; Everett, Martin G.; Johnson, Jeffrey C. Analyzing social networks. Sage, 2018.
Rihn, Jennifer; Oliver, Jonathan James. Detection of abusive user accounts in social networks. U.S. Patent Application No 10/116,614, 2018.
De Nooy, Wouter; Mrvar, Andrej; Batagelj, Vladimir. Exploratory social network analysis with Pajek: Revised and expanded edition for updated software. Cambridge University Press, 2018.
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How to Cite
[1]
Mastykash, O. and Peleshchyshyn, A. 2020. General algorithm for searching user data in social media of the Internet. European Journal of Engineering and Technology Research. 5, 1 (Jan. 2020), 82–86. DOI:https://doi.org/10.24018/ejeng.2020.5.1.1708.
