Social Media Data in the Big Data Environment

Keywords: social media data, big data, social media, Internet statistics, public sentiments.

Abstract

The article contains results of a study of social media data (SMD) which, being distinct from conventional data by their origin, require special methods for collection, processing and analysis. As shown by a literature review, in spite of great many research publications devoted to social media research and big data analysis, the SMD potential as a big data component still remains inadequately explored.     

Two approaches to research and analysis of SMD were highlighted in course of the study, in which SMD are addressed as an object of Internet statistics and an object of big data. When SMD are explored as an object of Internet statistics, collection of anonymized data is performed using the services that have network protocols for collection and analysis of data on social media customers using statistical methods. When SMD are explored as an object of big data, the collection is performed mostly by artificial intellect, whereas the storage and processing is operated by databases designed for large scopes of data and software with statistical data processing applications.          

The social media most popular with users in 2020 were identified in the study. Statistical indicators for assessment of users’ feedback, available now for statistical assessments of social media communities, are given. The study revealed several problems which solutions would require, apart from a multifaceted and complex approach to collection and processing, highly competent teams of specialists in various subject fields, including experts in computations, experts in machine learning and statisticians.             

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References

1. Onlain 2020. Yak pandemiia vplynula na onlain-korystuvannia [Online 2020. What was the pandemic impact on online use]. Suspilne, October 29, 2020. Retrieved from https://suspilne.media/74631-onlajn-2020-ak-pandemia-vplinula-na-onlajn-koristuvanna/ [in |Ukrainian].
2. Schwaiger J., Hammerl T., Florian J., & Leist S. (2021). UR: SMART–A tool for analyzing social media content. Information Systems and e-Business Management, 19, 1275–1320. https://doi.org/10.1007/s10257-021-00541-4
3. Sarprasatham M. (2016). Big Data in Social Media Environment: A Business Perspective. Social Media Listening and Monitoring for Business Applications, pp. 70–93. https://doi.org/10.4018/978-1-5225-0846-5.ch004
4. Bing L., Chan K. C. C., & Ou C. (2014). Public sentiment analysis in Twitter data for prediction of a company’s stock price movements. 11th IEEE International Conference on e-Business Engineering (ICEBE 2014). Sun Yat-sen University, Guangzhou, China. Retrieved from https://research.tilburguniversity.edu/en/publications/public-sentiment-analysis-in-twitter-data-for-prediction-of-a-com
5. Budiharto W., & Meiliana M. (2018). Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis. Journal of Big Data, 5, 51. https://doi.org/10.1186/s40537-018-0164-1
6. Seltzer E. K., Horst-Martz E., Lu M., & Merchant R. M. (September, 2017). Public sentiment and discourse about Zika virus on Instagram. Public Health, 150, 170–175. https://doi.org/10.1016/j.puhe.2017.07.015
7. Ahmad T., Alvi A., & Ittefaq M. (2019). The use of social media on political participation among university students: an analysis of survey results from rural Pakistan. SAGE Open, July-September, 1–9. https://doi.org/10.1177/2158244019864484
8. Svidronova M., Kascakova A., & Bambusekova G. (2019). Social media in the presidential election campaign: Slovakia 2019. Administratie si Management Public, 33, 181–194.
9. Mayfield A. (2008). What is social media? Vol. 1.4. Retrieved from: https://tavaana.org/sites/default/files/what-is-social-media-uk.pdf
10. Zernetska O. (2009). Hlobalna politychna blohosfera – nova arena politychnoi komunikatsii. Politychnyi menedzhment [The global political blogosphere: a new arena of political communication]. Politychnyi menedzhment – Political Management, 2, 13–26. Retrieved from http://dspace.nbuv.gov.ua/bitstream/handle/123456789/59793/02-Zernetska.pdf?sequence=1 [in Ukrainian].
11. McAllister I. (2016). Internet use, political knowledge and youth electoral participation in Australia. Journal of Youth Studies, vol. 19, issue 9, 1220–1236. https://doi.org/10.1080/13676261.2016.1154936
12. World Health Organization. (September, 2020). Managing the COVID-19 infodemic: Promoting healthy behaviours and mitigating the harm from misinformation and disinformation. Joint statement by WHO, UN, UNICEF, UNDP, UNESCO, UNAIDS, ITU, UN Global Pulse, and IFRC. Retrieved from https://www.who.int/news/item/23-09-2020-managing-the-covid-19-infodemic-promoting-healthy-behaviours-and-mitigating-the-harm-from-misinformation-and-disinformation
13. World Health Organization. (2020). WHA 73. Retrieved from https://apps.who.int/gb/e/e_wha73.html
14. Alton M. K. Chew, & Gunasekeran D. V. (2021). Social Media Big Data: The Good, The Bad, and the Ugly (Un)truths. Front. Big Data, 4, 623794. https://doi.org/10.3389/fdata.2021.623794
15. Domalewska D. (2021). An analysis of COVID‑19 economic measures and attitudes: evidence from social media mining. Journal of Big Data, 8, 42. https://doi.org/10.1186/s40537-021-00431-z
16. Internet World Stat. Retrieved from https://www.internetworldstats.com/
17. Digital 2021: Global Owerview Report (January, 2021). Retrieved from https://datareportal.com/reports/digital-2021-global-overview-report
18. TikTok says it has passed 1 billion users. (2021). The Verge. Retrieved from https://www.theverge.com/2021/9/27/22696281/tiktok-1-billion-users
19. Freeman L. C. (2004). The Development of social network analysis a study in the sociology of science. Empirical Press Vancouver, BC Canada, pp. 3–4. Retrieved from https://www.researchgate.net/profile/Linton-Freeman-2/publication/239228599_The_Development_of_Social_Network_Analysis/links/54415c650cf2e6f0c0f616a8/The-Development-of-Social-Network-Analysis.pdf
20. Wu C., Buyya R., & Ramamohanarao K. (2016). Chapter 1. Big Data Analytics = Machine Learning + Cloud Computing. Big Data Principles and Paradigms. R. Buyya, R. N. Calheiros, A. V. Dastjerdi (Eds.). Elsevier, pp. 3–38. https://doi.org/10.1016/B978-0-12-805394-2.00001-5
21. Kharkovchuk O. Dynamika zrostannia audytorii sotsialnykh merezh: porivniuiemo kvartalni zvity DataReportal za 2020 i 2021 roky [Dynamics of growth in the audience of social networks: comparing quarterly reports for 2020 and 2021]. Webpromo, July 8, 2021. Retrieved from https://web-promo.ua/ua/blog/dinamika-rosta-auditorii-socialnyh-setej-cravnivaem-kvartalnye-otchety-datareportal-za-2020-i-2021-gody/ [in Ukrainian].
22. Marr B. How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read. Bernard Marr & Co. Retrieved from https://bernardmarr.com/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/
23. Data Never Sleeps: Here’s What Happens Every Minute Online [Infographic]. (October, 2021). Retrieved from https://hcsmmonitor.com/2021/10/05/data-never-sleeps-heres-what-happens-every-minute-online-infographic/
24. Kaufmann M. A. (2019). Big Data Management Canvas: A Reference Model for Value Creation from Data. Big Data Cogn. Comput., 3(1), 19. https://doi.org/10.3390/bdcc3010019

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Published
2021-12-21
How to Cite
OSAULENKO, O., & HOROBETS, O. (2021). Social Media Data in the Big Data Environment. Scientific Bulletin of the National Academy of Statistics, Accounting and Audit, (3-4), 23-31. Retrieved from https://nasoa-journal.com.ua/index.php/journal/article/view/246