The Big Data use in social media
Abstract
The digital revolution and the communication platforms provided by the web 2.0 virtual space era, such as social media, social networks, other tools and channels, create new opportunities for better marketing decisions based on user-generated data analysis. Every day customers of social media and other virtual tools are creating huge amounts of their actions caused data, and business lack management tools for the support of this process, which could create knowledge in the area of customer profiles and preferences deeper cognition. Growing numbers of social media users indicate the popularity of these communication tools among the information society, but science today lacks a deeper knowledge of social media generated data and other algorithms for this data usage. Therefore, the purpose of the article is defined as the development of the conceptual model of big data generated by social media usage in business. The formation of the conceptual model is based on the analysis of big data assumptions and application possibilities, social media classification peculiarities and different channel specifics, identification of big data analysis methods and analysis of large data applications generated by social media. The conceptual model creates preconditions for deeper knowledge of user-generated big data in nowadays widely used communication platforms, as well as creation of the decision support tool for marketing specialists in order to use big data from social media in deeper customer profile and preferences cognition. Methods employed in this research are: literature and other references analysis, synthesis and logical analysis of information, comparison of information, systemization and visualization.
Article in Lithuanian.
Didžiųjų duomenų panaudojimas socialinėje medijoje
Santrauka
Ilgą laiką literatūroje buvo pabrėžiama socialinių tinklų ir socialinių medijų kaip komunikacijos priemonių nauda ir panaudojimo galimybės. Tobulėjančios technologijos ir interneto sparta lėmė tai, jog socialinių tinklų populiarumas bei vartotojų kuriamo turinio ir duomenų kiekis sparčiai auga. Susidaro palankios sąlygos įmonėms šiuos duomenis analizuoti bei panaudoti priimant strateginius sprendimus. Šio darbo probleminis klausimas yra didžiųjų duomenų, kuriuos sugeneruoja socialinės medijos, panaudojimo galimybės rinkodaroje. Straipsnyje analizuojamos didžiųjų duomenų charakteristikos, socialinių medijų rūšys bei jų generuojamų duomenų panaudojimo galimybės ir rizikos bei analizės metodai, sudaromas socialinės medijos sukuriamų didžiųjų duomenų panaudojimo koncepcinis modelis. Straipsnyje taikomi mokslinės literatūros ir kitų informacijos šaltinių sisteminės analizės bei apibendrinimo metodai.
Reikšminiai žodžiai: didieji duomenys, didžiųjų duomenų analizė, socialinės medijos, socialiniai tinklai.
Keyword : big data, big data analytics, social media, social networks
How to Cite
Karpovičiūtė, R., & Sabaitytė, J. (2019). The Big Data use in social media. Mokslas – Lietuvos Ateitis / Science – Future of Lithuania, 11. https://doi.org/10.3846/mla.2019.9585
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Khade, A. A. (2016). Performing customer behavior analysis using big data analytics. Procedia Computer Science, 79, 986-992. https://doi.org/10.1016/j.procs.2016.03.125
Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Mahmoud Ali, W. K., Alam, M., … Gani, A. (2014, July 17). Big data: survey, technologies, opportunities, and challenges. The Scientific World Journal, 2014. Hindawi. https://doi.org/10.1155/2014/712826
Kune, R., Konugurthi, P. K., Agarwal, A., Chillarige, R. R., & Buyya, R. (2016). The anatomy of big data computing. Software: Practice and Experience, 46(1), 79-105. https://doi.org/10.1002/spe.2374
Liu, J., Li, J., Li, W., & Wu, J. (2016). Rethinking big data: a review on the data quality and usage issues. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 134-142. https://doi.org/10.1016/j.isprsjprs.2015.11.006
Liu, Y., Li, Z., Xiong, H., Gao, X., & Wu, J. (2010). Understanding of internal clustering validation measures. In Data Mining (ICDM), 2010 IEEE 10th International Conference on (pp. 911-916). IEEE. Sydney, Australia. https://doi.org/10.1109/ICDM.2010.35
Mcafee, A., & Brynjolfsson, E. (2012). Big data: the management revolution. Harward Business Review, 90(10), 60-68.
MIT Technology Review. (2012). How Obama’s team used big data to rally voters. Retrieved from https://www.technology-review.com/s/509026/how-obamas-team-used-big-data-to-rally-voters/
Olshannikova, E., Olsson, T., Huhtamäki, J., & Kärkkäinen, H. (2017). Cenceptualizing Big social data. Journal of Big Data, 4(1), 0-19. https://doi.org/10.1186/s40537-017-0063-x
Oussous, A., Benjelloun, F.-Z., Ait Lahcen, A., & Belfkih, S. (2018). Big Data technologies: A survey. Journal of King Saud University – Computer and Information Sciences, 30(4), 431-448. https://doi.org/10.1016/j.jksuci.2017.06.001
Pennacchiotti, M., & Popescu, A.-M. (2011). A machine learning approach to Twitter user classification. Icwsm, 11(2), 281-288.
Politaitė, S., & Sabaitytė, J. (2018). Didžiųjų duomenų naudojimas klientui pažinti. Mokslas – Lietuvos Ateitis / Science – Future of Lithuania, 10, 1-10. https://doi.org/10.3846/mla.2018.932
Rajaraman, V. (2016). Big data analytics. Resonance, 21(8), 695-716. https://doi.org/10.1007/s12045-016-0376-7
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