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The Statistical Methods for Analyzing Social Media Data
Tumanov O. O.

Tumanov, Oleksii O. (2020) “The Statistical Methods for Analyzing Social Media Data.” Business Inform 2:266–272.
https://doi.org/10.32983/2222-4459-2020-2-266-272

Section: Economic statistics

Article is written in Ukrainian
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UDC 303.71:[316.77:004.77](477)

Abstract:
Due to the spread of Internet technologies, two-way communication between people is becoming stronger and spreading not only in a certain place, but also in all corners of our world. Increased use of social media generates large amounts of data and new types of data that were not previously available. The concepts of «social networks» and «social media» are increasingly part of social discussions, organizational strategy and scientific research. The growing interest in social networks is coupled with the dissemination of widely available network data, there is a significant increase in methods and understanding of how to analyze social media data and where the results of such researches can be used. The use of this data can be of key nature in modern sociology and be important in economics, anthropology, biology, demographics, communication research, geography, history, informatics, organizational research, political science, health, social psychology, outlook studies, sociolinguistics and others. It is an present-time affordable consumer tool, so the growing interest in the analysis and modeling of social data, as well as the relevance of this topic should not be underestimated. This article presents the methods used to analyze a wide range of social media based on relationship, business cooperation, political alliances, etc. In this context, network structure and internal information structures can be decisive. In some cases, they can have a positive impact on the efficiency of economic growth, while in others, the network structure can have destructive consequences due to the existence of many links between some central actors (e.g., the spread of phenomena such as epidemics or financial crises). This article is an introduction to consideration of the statistical models designed to examine the specifics of social media data and the effects of social processes. Approaches to the analysis of the collected data are considered, as well as issues of reliability and reliability of the results are brought up for discussion.

Keywords: analysis methods, data collection, social media, analysis, social networks.

Fig.: 1. Tabl.: 1. Bibl.: 18.

Tumanov Oleksii O. – Applicant, Department of Statistics, Accounting and Auditing, V. N. Karazin Kharkiv National University (4 Svobody Square, Kharkіv, 61022, Ukraine)
Email: [email protected]

List of references in article

Voitovych, O., Buda, A., and Holovenko, V. “Doslidzhennia metodiv analizu sotsialnykh merezh yak seredovyshcha informatsiinykh viin“ [Analyses Methods Research of Social Networks as a Environment of Warfare]. Metody ta zasoby koduvannia, zakhystu i ushchilnennia informatsii. 2017. https://ir.lib.vntu.edu.ua/bitstream/handle/123456789/27958/76-86ycc0hnc6o8o3xgkr97hrynqd5m0obr.pdf?sequence=1&isAllowed=y
Gorchinskaya, O., and Rivkin, A. “Analiz dannykh sotsialnykh setey“ [Social Network Data Analysis]. Otkrytyye sistemy. 2015. http://www.fors.ru/upload/smi/Open-Systems_Gorchinskaya-Rivkin_09-2015.pdf
Mazurenko, V. V., and Shtovba, S. D. “Ohliad modelei analizu sotsialnykh merezh“ [Overview of Models for Social Network Analysis]. Visnyk Vinnytskoho politekhnichnoho instytutu. 2015. https://ir.lib.vntu.edu.ua/bitstream/handle/123456789/11950/813-812-1-PB.pdf?sequence=1&isAllowed=y
Adeli, H., and Siddique, N. “Introduction to Computational Intelligence“. In Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing, 1-17. Oxford, UK: John Wiley & Sons Ltd, 2013.
“Social Network Data Analytics“. Boston, MA : Springer US, 2011. http://www.charuaggarwal.net/socialtoc.pdf
Amodei, D. et al. “Concrete Problems in AI Safety“. 2016. https://arxiv.org/pdf/1606.06565.pdf
Armour, F. et al. “Big Data: Issues and Challenges Moving Forward“. 46th Hawaii International Conference on System Sciences, 2013. 995-1004. DOI: 10.1109/HICSS.2013.645
Armstrong, M. et al. “Inferring Latent User Properties from Texts Published in Social Media“. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015. https://www.cs.jhu.edu/~svitlana/papers/VBAS-aaai15-demo.pdf
Cambria, E., and Hussain, A. Introduction in Sentic Computing. Springer International Publishing, 2015.
Chen, B.-W. et al. “Face hallucination and recognition in social network services“. The Journal of Supercomputing, vol. 71, no. 6 (2015): 2035-2049. DOI: 10.1007/s11227-014-1257-z
Csardi, G., and Kolaczyk, E. “Statistical Analysis of Network Data“. R. Journal of Statistical Software, vol. 66 (2015), book review 1. DOI: 10.18637/jss.v066.b01
D'Orazio, F. “The future of social media research: or how to re-invent social media listening in 10 steps. 2013“. Pulsar. https://www.pulsarplatform.com/resources/the-future-of-social-media-research/
Gandomi, A., and Haider, M. “Beyond the hype: Big data concepts, methods, and analytics“. International Journal of Information Management, vol. 35, no. 2 (2015): 137-144. DOI: https://doi.org/10.1016/j.ijinfomgt.2014.10.007
Hansen, D., Shneiderman, B., and Smith, M. “Analyzing Social Media Networks with NodeXL: Insights from a Connected World“. Morgan Kaufmann, 2011. https://www.sciencedirect.com/book/9780123822291/analyzing-social-media-networks-with-nodexl#book-info
Ruths, D., and Pfeffer, J. “Social media for large studies of behavior“. Science. 2014. https://people.cs.umass.edu/~brenocon/smacss2015/papers/Science-2014-Ruths-1063-4.pdf
Lin, H. et al. “Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks“. Algorithms, vol. 9, no. 2 (2016): 41-52. DOI: 10.3390/a9020041
“AI Is Transforming Google Search. The Rest of the Web is Next“. WIRED, 2016. https://www.wired.com/2016/02/ai-is-changing-the-technology-behind-google-searches/
Tufecki, Z. “Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls“. ICWSM'14: Proceedings of the 8th International AAAI Conference on Weblogs and Social Media, 2014. https://arxiv.org/abs/1403.7400

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