Share:


Didžiųjų duomenų panaudojimas socialinėje medijoje / 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.


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

Published in Issue
Jun 14, 2019
Abstract Views
137
PDF Downloads
71
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Adweek. (2018). How marketing teams can unleash personalized creative at scale. Retrieved from https://www.adweek.com/digital/how-marketing-teams-can-unleash-personalized-creative-at-scale/

Agarwal, S. (2013). Data mining: data mining concepts and techniques. In Machine Intelligence and Research Advancement (ICMIRA), 2013 International Conference. Katra, India. https://doi.org/10.1109/ICMIRA.2013.45

Aggarwal, C. C. (2011). An introduction to social network data analytics. Social network data analytics. Springer, Boston, MA. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.673.3407&rep=rep1&type=pdf

Agichtein, E., Castillo, C., Donato, D., Gionis, A., & Mishne, G. (2008). Finding high-quality content in social media. In Proceedings of the 2008 international conference on web search and data mining (pp. 183–193). Palo Alto, California. https://doi.org/10.1145/1341531.1341557

Ahmed, A. B. E. D., & Elaraby, I. S. (2014). Data mining: a prediction for student’s performance using classification method. World Journal of Computer Application and Technology, 2(2), 43-47. https://doi.org/10.13189/wjcat.2014.020203

Alharthi, A., Krotov, V., & Bowman, M. (2017). Addressing barriers to big data. Business Horizons, 60(3), 285-292. https://doi.org/10.1016/j.bushor.2017.01.002

Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2018). Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics (In Press, corrected proof). https://doi.org/10.1016/j.aci.2018.05.004

Banerjee, S., & Agarwal, N. (2012). Analyzing collective behavior from blogs using swarm intelligence. Knowledge and Information Systems, 33, 523-547. https://doi.org/10.1007/s10115-012-0512-y

Barbu, O. (2014). Advertising, microtargeting and social media. Procedia-Social and Behavioral Sciences, 163, 44-49. https://doi.org/10.1016/j.sbspro.2014.12.284

Batrinca, B., & Treleaven, P. C. (2015). Social media analytics: a survey of techniques, tools and platforms. AI & Soc, 30, 89-116. https://doi.org/10.1007/s00146-014-0549-4

Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: recent achievements and new challenges. Information Fusion, 28, 45-59. https://doi.org/10.1016/j.inffus.2015.08.005

Boerman, S. C. (2016). Political microtargeting: relationship between personalized advertising on Facebook and voters’ responses. Cyberpsychology, Behavior, and Social Networking, 19(6), 367-372. https://doi.org/10.1089/cyber.2015.0652

Boyd, D., & Crawford, K. (2012). Critical question for Big Data. Information, Communication & Society, 15(5), 662-679. https://doi.org/10.1080/1369118X.2012.678878

Canick, H. (2016). How social media, microtargeting and big data revolutionized political marketing. Retrieved from https://www.ama.org/publications/MarketingNews/Pages/social-media-big-data-microtargeting-revolutionized-political-marketing.aspx?utm_content=buffer519bf&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

Cavanillas, J. M., Curry, E., & Wahlster, W. (Eds.). (2015). New horizons for a data-driven economy – a roadmap for Big Data in Europe. Springer, Cham. https://doi.org/10.1007/978-3-319-21569-3

Chen, Y.-C., Peng, W.-C., & Lee, S.-Y. (2012). Efficient algorithms for influence maximization in social networks. Knowledge and Information Systems, 33, 577-601. https://doi.org/10.1007/s10115-012-0540-7

Čičević, S., Samčović, A., & Nešić, M. (2016). Exploring college students’ generational differences in Facebook usage. Computers in Human Behavior, 56, 83-92. https://doi.org/10.1016/j.chb.2015.11.034

Constantinides, E. (2014). Foundations of social media marketing. Procedia-Social and Behavioral Sciences, 148, 40-57. https://doi.org/10.1016/j.sbspro.2014.07.016

Constantinides, E., & Fountain, S. J. (2008). Special issue papers Web 2.0: Conceptual foundations and marketing issues. Journal of Direct, Data and Digital Marketing Practice, 9, 231-244. https://doi.org/10.1057/palgrave.dddmp.4350098

Couldry, N., & Turow, J. (2014). Advertising, big data and the clearance of the public realm: marketers’ new approaches to the content subsidy. International Journal of Communication, 8, 1710-1726.

Cuzzocrea, A., Song, I.-Y., & Davis, K. C. (2011). Analytics over large-scale multidimensional data: The Big Data revolution! In Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP (pp. 101-104). ACM. https://doi.org/10.1145/2064676.2064695

Dong, X. L., & Srivastava, D. (2013). Big data integration. In Data Engineering (ICDE), 2013 IEEE 29th International Conference on (pp. 1245-1248). IEEE. https://doi.org/10.1109/ICDE.2013.6544914

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007

Gartner. (2015). Gartner says business intelligence and analytics leaders must focus on mindsets and culture to kick start advanced analytics. Retrieved from https://www.gartner.com/newsroom/id/3130017

Groves, P., Kayyali, B., Knott, D., Kuiken ir S. Van. (2013). The „big data“ revolution in healthcare. McKinsey Quarterly, 2(3).

Gruebner, O., Sykora, M., Lowe, S. R., Shankardass, K., Galea, S., & Subramanian, S. V. (2017). Big data opportunities for social behavioral and mental health research. Social Science and Medicine, 189, 167-169. Retrieved from https://dspace.lboro.ac.uk/dspace-jspui/bitstream/2134/26030/3/gruebner et al_ big_data_v-def.pdf

Hashem, I. A. T., Yaqoob, I., Anuar, B., Mokhtar, S., Gani, A. ir Khan, U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, (47), 98-115. https://doi.org/10.1016/j.is.2014.07.006

He, W., Zha, S. ir Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472. https://doi.org/10.1016/j.ijinfomgt.2013.01.001

Henderson, A. (2010). Authentic dialogue? The role of “friendship” in a social media recruitment campaign. Article in Journal of Communication Management, 14(3). https://doi.org/10.1108/13632541011064517

Internet World Stats. (2018). World internet usage and population statistics. Retrieved from https://www.internetworldstats.com/stats.htm

Kaisler, S., Armour, F., Espinosa, J. A. ir Money, W. (2014). Big data: Issues and challenges moving forward. Proceedings of the Annual Hawaii International Conference on System Sciences (pp. 995-1004). Wailea, USA. https://doi.org/10.1109/HICSS.2013.645

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53, 59-68. https://doi.org/10.1016/j.bushor.2009.09.003

Kaplan, A. M., & Haenlein, M. (2011). Two hearts in three-quarter time: how to waltz the social media/viral marketing dance. Business Horizons, 54(253-263). https://doi.org/10.1016/j.bushor.2011.01.006

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

Russom, P., & Org, T. (2011). Big Data Analytics.

Senthilkumar, A. A., Rai, B. K., Meshram, A. A., & Gunasekaran, A. (2018). Big Data in healthcare management: a review of literature. American Journal of Theoretical and Applied Business, 4(2), 57-69. https://doi.org/10.11648/j.ajtab.20180402.14

Sharma, S. (2015). Rise of Big Data and related issues. In 2015 Annual IEEE India Conference (INDICON) (pp. 1-6). IEEE. New Delhi, India. https://doi.org/10.1109/INDICON.2015.7443346

SimilarWeb. (2019). Website traffic statistics & market intelligence. Retrieved from https://www.similarweb.com/

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286. https://doi.org/10.1016/j.jbusres.2016.08.001

Statista. (2017). Number of social media users worldwide 2010–2021. Retrieved from https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/

Statista. (2018). User-generated internet content per minute 2018. Retrieved from https://www.statista.com/statistics/195140/new-user-generated-content-uploaded-by-users-per-minute/

Tanwar, M., Duggal, R., & Khatri, S. K. (2015). Unravelling unstructured data: A wealth of information in big data. In 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions) (pp. 1-6). IEEE. https://doi.org/10.1109/ICRITO.2015.7359270

Thevenot, G. (2007). Blogging as a social media. Tourism and Hospitality Research, 7(3-4), 287-289. https://doi.org/10.1057/palgrave.thr.6050062

Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. https://doi.org/10.1016/j.ijpe.2014.12.031

Wang, Y., Wang, S., Tang, J., Liu, H., & Li, B. (2015). Unsupervised sentiment analysis for social media images. In IJCAI International Joint Conference on Artificial Intelligence, 2015, 2378-2379. https://doi.org/10.1109/ICDMW.2015.142

We Are Social. (2018). Global Digital Report 2018. Retrieved from https://digitalreport.wearesocial.com/

Weiguo, F., & Gordon, M. D. (2014). Unveiling the power of social media analytics. Communications of the ACM, 57(6). https://doi.org/10.1145/2602574

Weiss, S. M., & Indurkhya, N. (1998). Predictive data mining: a practical guide. Morgan Kaufmann Publishers.

Wildman, S., & Obar, J. A. (2015). Social media definition and the governance challenge: An introduction to the special issue. Telecommunications Policy, 39(9), 745-750. https://doi.org/10.2139/ssrn.2637879

Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2003). Data mining with big data Xindong. IEEE Transactions on Knowledge and Data Engineering, 15(2), 353-367. https://doi.org/10.1109/TKDE.2013.109

Yin, S., & Kaynak, O. (2015). Big data for modern industry: challenges and trends [point of view]. Proceedings of the IEEE, 103(2), 143-146. https://doi.org/10.1109/JPROC.2015.2388958

Zhu, Y.-Q., & Chen, H.-G. (2015). Social media and human need satisfaction: Implications for social media marketing. Business Horizons, 58, 335-345. https://doi.org/10.1016/j.bushor.2015.01.006