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Identification of User Patterns in Social Networks by Data Mining Techniques: Facebook Case

  • A. Selman Bozkır
  • S. Güzin Mazman
  • Ebru Akçapınar Sezer
Part of the Communications in Computer and Information Science book series (CCIS, volume 96)

Abstract

Currently, social networks such as Facebook or Twitter are getting more and more popular due to the opportunities they offer. As of November 2009, Facebook was the most popular and well known social network throughout the world with over 316 million users. Among the countries, Turkey is in third place in terms of Facebook users and half of them are younger than 25 years old (students). Turkey has 14 million Facebook members. The success of Facebook and the rich opportunities offered by social media sites lead to the creation of new web based applications for social networks and open up new frontiers. Thus, discovering the usage patterns of social media sites might be useful in taking decisions about the design and implementation of those applications as well as educational tools. Therefore, in this study, the factors affecting “Facebook usage time” and ”Facebook access frequency” are revealed via various predictive data mining techniques, based on a questionnaire applied on 570 Facebook users. At the same time, the associations of the students’ opinions on the contribution of Facebook in an educational aspect are investigated by employing the association rules method.

Keywords

Social networks decision trees Facebook association rules 

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References

  1. 1.
    Bartlett-Bragg, A.: Reflections on Pedagogy: Reframing Practice to Foster Informal Learning with Social Software (2006), http://www.dream.sdu.dk/uploads/files/Anne%20Bartlett-Bragg.pdf
  2. 2.
    boyd, D.M., Ellison, N.B.: Social Network Sites: Definition, History, and Scholarship. Journal of Computer-Mediated Communication 13, 210–230 (2007)CrossRefGoogle Scholar
  3. 3.
    Lenhart, M.: Adults and Social Network Websites. Pew Internet & American Life Project Report (2009), http://www.pewinternet.org/pdfs/PIP_Adult_social_networking_data_memo_FINAL.pdf
  4. 4.
    Bumgarner, B.A.: You Have Been Poked: Exploring the Uses and Gratifications of Facebook Among Emerging Adults. First Monday, 22 (2007), http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/viewArticle/2026/1897
  5. 5.
    Mejias, U.: Nomad’s Guide to Learning and Social Software (2005), http://knowledgetree.flexiblelearning.net.au/edition07/download/la_mejias.pdf
  6. 6.
    English, R., Duncan-Howell, J.: Facebook Goes to College: Using Social Networking Tools to Support Students Undertaking Teaching Practicum. Journal of Online Learning and Teaching 4, 596–601 (2008)Google Scholar
  7. 7.
    Lockyer, L., Patterson, J.: Integrating Social Networking Technologies in Education: A Case Study of a Formal Learning Environment. In: Proceedings of 8th IEEE International Conference on Advanced Learning Technologies, Spain, pp. 529–533 (2008)Google Scholar
  8. 8.
    Ajjan, H., Hartshorne, R.: Investigating Faculty Decisions to Adopt Web 2.0 Technologies: Theory and Empirical Tests. The Internet and Higher Education 11, 71–80 (2008)CrossRefGoogle Scholar
  9. 9.
    Saunders, S.: The Role of Social Networking Sites in Teacher Education Programs: A Qualitative Exploration. In: McFerrin, K., et al. (eds.) Proceedings of Society for Information Technology and Teacher Education International Conference, pp. 2223–2228. AACE, Chesapeake (2008)Google Scholar
  10. 10.
    Mazman, S.G., Usluel, Y.K.: Adoption Process of Social Network and Their Usage in Educational Context. Master Thesis. The Institute for Graduate Studies in Science and Engineering. Hacettepe University, Ankara (2009)Google Scholar
  11. 11.
    Selwyn, N.: Web 2.0 Applications as Alternative Environments for Informal Learning - A Critical Review. Alternative Learning Environments in Practice: Using ICT to Change Impact and Outcomes, OECD-KERIS Expert Meeting (2007)Google Scholar
  12. 12.
    Check Facebook (2009), http://www.checkfacebook.com
  13. 13.
    Berry, M., Linoff, G.: Mastering Data Mining: The Art and Science of Customer Relationship Management. John Wiley & Sons, Chichester (2000)Google Scholar
  14. 14.
    Bozkir, A.S., Gök, B., Sezer, E.: İnternetin Eğitimsel Amaçlar için Kullanımını Etkileyen Faktörlerin Veri Madenciliği Yöntemleriyle Tespiti. In: Bilimde Modern Yöntemler Sempozyumu, pp. 833–842. Eskişehir (2008)Google Scholar
  15. 15.
    Tang, Z., MacLennan, J.: Data Mining with SQL Server 2005. John Wiley & Sons, Indiana (2005)Google Scholar
  16. 16.
    Kass, G.V.: An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics 29, 119–127 (1980)CrossRefGoogle Scholar
  17. 17.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, P.J.: Classification and Regression Trees. Wadsworth International Group, Belmont (1984)zbMATHGoogle Scholar
  18. 18.
    Liu, J., Wang, Z., Xiao, X.: A Hybrid SVM/DDBHMM Decision Fusion Modeling for Robust Continuous Digital Speech Recognition. Pattern Recognition Letters 28, 912–920 (2007)CrossRefGoogle Scholar
  19. 19.
    Fuller, C.M., Piros, D.P., Wilson, R.L.: Decision Support for Determining Veracity via Linguistic-Based Cues. Decision Support Systems 46, 697–703 (2009)CrossRefGoogle Scholar
  20. 20.
    Romero, C., Ventura, S.: Educational Data Mining: A survey from 1995 to 2005. Expert Systems with Applications 33, 135–146 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • A. Selman Bozkır
    • 1
  • S. Güzin Mazman
    • 2
  • Ebru Akçapınar Sezer
    • 1
  1. 1.Department of Computer EngineeringHacettepe UniversityAnkaraTurkey
  2. 2.Department of Computer Education and Instructional TechnologiesHacettepe UniversityAnkaraTurkey

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