Cultural Context In Process Of Mining Data From Social Media – Recommendations Based On Literature Review

Joanna Michalak, Patrycja Gulak-Lipka


Social media is nothing else than a modern communication channel that carry a lot of advantages, such as their reach or range. Social media has such a big power of its reach that a single post, tweet, or "broad" start to matter globally. With globalization, we have seen an increase in usage of social media everywhere. This means that communication is being conducted across the borders or different countries, continents or even cultures. It is an desirable effect, however the social media user across the world differs in respect to their culture and data shows that significant differences exist in a way people in the world social media. However, in order to be well prepared to dig in social media, the question should be post whether the cultural context affects the activity of users. If so, it is appropriate to prepare data filters to include some specific criteria. In first part authors apply the Cross - Industry Standard Process for Data Mining (CRISP-DM) in social media data to specify the process of data analysis. Second part focuses on recommendations about cultural context in mining social media.


social media, Twitter, CRISP-DM, cultural context, Web 2.0.

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Acar A., Deguchi A., (2013), "Culture and social media usage: Analysis of Japanese twitter users", International Journal of Electronic Commerce Studies, Vol.4, No.1, pp.21-32, 2013.

Bokszański Z., (2007), Indywidualizm a zmiana społeczna, Wydawnictwo Naukowe PWN.

Bonzanini, M. Mastering Social Media Mining with Python, retrieved from: [27.08.2016].

Ebner M. & Schiefner M., (2008), Microblogging—more than fun? In: I. Arnedillo Sánchez & P. Isaías (Eds.), Proceedings of the IADIS Mobile Learning Conference (p155-159). Lisbon, Portugal: IADIA.

Egros, A., Social Media Usage Across Cultures, retrieved from:

Giachanou, A. & Crestani, F., (2016) Like it or not: A survey of Twitter sentiment analysismethods , ACM Comput. Surv. 49, 2, Article 28.

Gulak-Lipka P., (2016), Intercultural management on the basis of a sports club, Acta UNCZarz., Z. 43 No. 3, pp. 63-80.

Hall E.T., (2001), Poza kulturą, Warszawa, Wydawnictwo PWN.

Kamińska-Radomska I., (2012), Kultura biznesu, normy i formy, Wydawnictwo Naukowe PWN, Warszawa.

Khaleel M.A., (2013), A Survey of Data Mining Techniques on Medical Data for Finding Locally Frequent Diseases, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, No. 12, pp. 149-153.

Kowalczyk S., (2010), Elementy filozofii i teologii sportu, Wydawnictwo Katolicki Uniwersytet Lubelski.

Kulikowski K., (2015), Zastosowanie modelu CROSS INDUSTRY STANDARD PROCESS FOR DATA MINING (CRISP-DM) W badaniach postaw I opinii pracowników., Zeszyty Naukowe Politechniki śląskiej, Seria: Organizacja i Zarządzanie z 82, No. Kol. 1940, pp. 111-121.

Larose D.T., (2006), Data mining methods and models, Wileyo-Intesscience A John Wiley & Sons, INC Publications, retrieved from: html?id=58923307217e20ed3b2cc056 HYPERLINK

"https://www.researchgate.netfile.PostFileLoader.html?id=58923307217e20ed3b2cc056&assetKey=AS:457031996973056@148597632733"& HYPERLINK


Michalak J., (2016), Detecting sentiment in Twitter data – supervised machine learning approach for Twitter Sentiment Analysis in Python, Torun Business Review, v 15, No. 4, pp. 97-110.

Mikuła B., (2015), Współczesne tendencje w zachowaniach organizacyjnych, Uniwersytet Ekonomiczny w Krakowie.

Riedel M., Memon A.S. & Memon M.S., (2014) High productivity data processing analytics methods with applications, "Information and Communication Technology, Electronics and Microelectronics (MIPRO), 37th International Convention, pp. 289-294.

Rivo E., De La Fuente J., Rivo Á., García-Fontán E., Cañizares M.Á. & Gil P. (2012) CrossIndustry Standard Process for data mining is applicable to the lung cancer surgery domain, improving decision making as well as knowledge and quality management. Clinical and Translational Oncology, 14, pp. 73-79.

Smit C., (2014), How to overcome cultural differences in business Avoid the Mistakes that Everyone Else is Making When Doing Business Internationally, Amazon Digital Services LLC.

Smit C., (2015), Uncertainty Avoidance in international business, The Hidden Cultural Dimension You Need to Understand When Doing Business Overseas, Amazon Digital Services LLC.

Venter J., de Waal A. & Willers C., (2007), Specializing CRISP-DM for evidence mining, [in:] P. Craiger, S. & Shenoi, S. (eds.) Advances in Digital Forensic III pp. 303-315, Springer, Boston.

Xiao X., Xu H. & Xu S. (2015), Using IBM SPSS modeler to improve undergraduate mathematical modelling competence. Computer Applications in Engineering Education, in Press.



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Print ISSN: 1643-8175, Online ISSN: 2451-0955, DOI prefix: 10.19197, Principal Contact: