Using Social Network Analysis to Identify User Preferences for Cultural Events

  • Stevan Milovanović
  • Zorica Bogdanović
  • Aleksandra Labus
  • Dušan Barać
  • Marijana Despotović-Zrakić
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 745)

Abstract

The subject of this paper is social network analysis and its possible application in the field of culture. The main goal is to investigate the potentials of social network analysis for detecting user profiles and preferences regarding the certain types of cultural events. Data was collected through a mobile application and a survey. The results were analyzed using Ucinet and Vosviewer tools. The results reveal three clusters of events, as well as the most influential users and their preferences towards certain types of cultural events. The obtained results can be used to define future marketing activities, such as customization of offers regarding the identified users’ preferences.

Keywords

Social computing Social network analysis User preferences Cultural events 

Notes

Acknowledgement

Authors are thankful to Ministry of education, science and technological development, Republic of Serbia, grant 174031.

References

  1. 1.
    Fernando, M., Ginige, A., Hol, A.: Structural and behavioural models for social computing applications. In: 27th Australasian Conference on Information Systems, no. 53, University of Wollongong, Wollongong (2016)Google Scholar
  2. 2.
    Selloni, D.: CoDesign for Public-Interest Services, Milan (2017)CrossRefGoogle Scholar
  3. 3.
    Abraham, A., Hassanien, A.: Computational Social Networks: Tools, Perspectives and Applications. Springer Science & Business Media, Auburn (2012)Google Scholar
  4. 4.
    Dasgupta, S.: Social Computing: Concepts, Methodologies, Tools, and Applications. IGI Global, New York (2009)Google Scholar
  5. 5.
    Parameswaran, M., Whinston, A.B.: Social computing: an overview. J. CAIS 19, Article no. 37 (2007)Google Scholar
  6. 6.
    Osman, I.H.: Handbook of Research on Strategic Performance Management and Measurement Using Data Envelopment Analysis. IGI Global, Hershey (2013)Google Scholar
  7. 7.
    Khosrow-Pour, M.: Social Computing in Encyclopedia of Information Science and Technology, 3rd edn, p. 6754 (2014)Google Scholar
  8. 8.
    Fernando, M., Ginige, A., Hol, A.: Enhancing business outcomes through social computing. J. IADIS Int. J. 14, 91–108 (2016)Google Scholar
  9. 9.
    Podobnik, V., Lovrek, I.: Implicit social networking: discovery of hidden relationships, roles and communities among consumers. J. Procedia Comput. Sci. 60, 583–592 (2015)CrossRefGoogle Scholar
  10. 10.
    Gross, R., Acquisti, A.: Information revelation and privacy in online social networks (The Facebook case). In: ACM Workshop on Privacy in the Electronic Society, Alexandria, USA, pp. 71–80 (2005)Google Scholar
  11. 11.
    Cachia, R.: Social Computing: Study on the Use and Impact of Online Social Networking, Luxembourg (2008)Google Scholar
  12. 12.
    Brandão, M.A., Moro, M.M.: Social professional networks: a survey and taxonomy. J. Comput. Commun. 100, 20–31 (2017)CrossRefGoogle Scholar
  13. 13.
    Schlattmann, S.: Capturing the collaboration intensity of research institutions using social network analysis. Procedia Comput. Sci. 106, 25–31 (2017)CrossRefGoogle Scholar
  14. 14.
    Erfanmanesh, M., Hosseini, E.: Using social network analysis method to visualize library & information science research. J. Adv. Inf. Technol. 7(3), 177–182 (2016)CrossRefGoogle Scholar
  15. 15.
    de-Marcos, L., García-López, E., García-Cabot, A., Medina-Merodio, J., Domínguez, A., Martínez-Herráiz, J., Diez-Folledo, T.: Social network analysis of a gamified e-learning course: small-world phenomenon and network metrics as predictors of academic performance. J. Comput. Hum. Behav. 60, 312–321 (2016)CrossRefGoogle Scholar
  16. 16.
    Get Started with Analytics for Android. https://firebase.google.com/docs/analytics/android/start/
  17. 17.
    Borgatti, S.P., Everett, M.G., Freeman, L.C.: UCINET for windows: software for social network analysis. J. Analytic Technologies, Harvard, MA (2002)Google Scholar
  18. 18.
    Van Eck, N.J., Waltman, L.: VOSviewer Manual: Pajek NET Format. https://gephi.org/users/supported-graph-formats/pajek-net-format/
  19. 19.
    UCINET 6 for Windows Help – Normalization. http://www.analytictech.com/ucinet/help/1mmzzrm.htm
  20. 20.
    UCINET 6 for Windows Help – Hierarchical cluster analysis. http://www.analytictech.com/ucinet/help/3j.x0e.htm
  21. 21.
    Batagelj, V., Mrvar, A.: Pajek: program for analysis and visualization of large networks. In: Timeshift-The World in Twenty-Five Years: Ars Electronica, pp. 242–251 (2004)Google Scholar
  22. 22.
  23. 23.
    Van Eck, N.J., Waltman, L.: How to normalize cooccurrence data? An analysis of some well-known similarity measures. J. Am. Soc. Inf. Sci. Technol. 60, 1635–1651 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Stevan Milovanović
    • 1
  • Zorica Bogdanović
    • 1
  • Aleksandra Labus
    • 1
  • Dušan Barać
    • 1
  • Marijana Despotović-Zrakić
    • 1
  1. 1.Faculty of Organizational SciencesUniversity of BelgradeBelgradeSerbia

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