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Modeling and OLAPing social media: the case of Twitter

  • Maha Ben KraiemEmail author
  • Jamel Feki
  • Kaïs Khrouf
  • Franck Ravat
  • Olivier Teste
Original Article
Part of the following topical collections:
  1. Social Network Analysis and Information Systems

Abstract

In the recent year, social networks have revolutionized the ways of interacting and exchanging information on the Internet. Millions of users interact frequently and share variety of digital content with each other. They express their feelings and opinions on every topic of interest. These opinions carry import value for personal, academic, and commercial applications, but the volume and the speed at which these are produced make it a challenging task for researchers and the underlying technologies to provide useful insights into such data. We attempt to extend the established online analytical processing (OLAP) technology to allow multidimensional analysis of social media data. In this paper, we pursue a goal of providing a generic multidimensional model dedicated to the OLAP of social media and specially Twitter. The proposed model reflects on some specifics such as recursive references between tweets, Empty dimension, and different types of hierarchies. It is implemented using NetBeans IDE platform. We present also some experimental results. We expect our proposed approach to be applicable for analyzing the data of other social networks as well.

Keywords

Twitter Tweets Multidimensional model OLAP 

References

  1. Bifet A, Holmes G, Pfahringer B, Gavaldà R (2011) Detecting sentiment change in Twitter streaming data. In: 2nd workshop on applications of pattern analysis, JMLR: workshop and conference proceedings 17, pp 5–11Google Scholar
  2. Bouillot F, Poncelet P, Roche M (2012) How and why exploit tweet’s location information? In: Proceedings of the AGILE’2012 international conference on geographic information science, Avignon, 24–27 Apr, ISBN: 978-90-816960-0-5Google Scholar
  3. Bringay S, Béchet N, Bouillot F, Poncelet P, Roche M, Teisseire M (2011) Towards an on-line analysis of Tweets processing. In: 22nd international conference on database and expert systems applications, DEXA, ToulouseGoogle Scholar
  4. Chaudhuri S, Dayal U (1997) Data warehousing and OLAP for decision support. In: DOOD, pp 33–34Google Scholar
  5. Cuvelier E, Aufaure MA (2011) A buzz and e-reputation monitoring tool for twitter based on galois lattices. Concept Struct Discov Knowl 6828:91–103CrossRefGoogle Scholar
  6. Guille A, Favre C (2015) Event detection, tracking and visualization in twitter: a mention-anomaly-based approach. In: Social network analysis and mining no. SNAM-D-14-00102R1Google Scholar
  7. Java A, Song X, Finin T, Tseng B (2007) Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD workshop on web mining and social network analysis, ACM, pp 56–65Google Scholar
  8. Kalucki J (2010) Twitter streaming API. http://apiwiki.twitter.com/Streaming-API-Documentation
  9. Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of social media. Bus Horiz 53(1):61CrossRefGoogle Scholar
  10. Kimball R (1996)  The Data Warehouse Toolkit. Wiley, New York. ISBN 978-0-471-15337-5Google Scholar
  11. Kraiem MB, Feki J, Khrouf K, Ravat F, Teste O (2014) OLAP of the Tweets: from modeling toward exploitation. In: 8th international conference on research challenges in information science (IEEE RCIS’2014), Marrakesh, pp 45–55, 28–30 May 2014, ISBN #978-1-4799-2393-9Google Scholar
  12. Kumar S, Morstatter F, Marshall G, Liu H, Nambiar U (2012) Navigating information facets on twitter (NIF-T). In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, 12–16 Aug, BeijingGoogle Scholar
  13. Lampos V, Lansdall-Welfare T, Araya R, Cristianini N (2013) Analysing mood patterns in the United Kingdom through Twitter content, arXiv preprint arXiv:1304.5507Google Scholar
  14. Liu X, Tang K, Hancock J, Han J, Song M, Xu R, Pokorny B (2013) A text cube approach to human, social and cultural behavior in the twitter stream. In: Proceedings of the 6th international conference on social computing, behavioral-cultural modeling and predictionGoogle Scholar
  15. Mansmann S, Rehman N, Weiler A, Scholl MH (2014) Discovering OLAP dimensions in semi-structured data. Inf Syst (2014 close proximity). http://dx.doi.org/10.1016/j.is.2013.09.002i
  16. Martínez V, González VM (2013) Sentiment characterization of an urban environment via Twitter. In: Urzaiz G, Ochoa SF, Bravo J, Chen LL, Oliveira J (eds) Ubiquitous computing and ambient intelligence. Context-awareness and context-driven interaction, Springer, Berlin, pp 394–397Google Scholar
  17. Mathioudakis M, Koudas N (2010) Twittermonitor: trend detection over the twitter stream. In Proceedings of international conference on management of data, SIGMOD 2010Google Scholar
  18. Phelan O, McCarthy K, Smyth B (2009) Using twitter to recommend real-time topical news. In: Proceedings of the third ACM conference on recommender systems. pp 385–388Google Scholar
  19. Quercia D, Kosinski M, Stillwell D, Crowcroft J (2011) Our twitter profiles, our selves: Predicting personality with twitter. In: IEEE international conference on social computing, pp 180–185Google Scholar
  20. Ravat F, Teste O, Tournier R, Zurfluh G (2008) Algebraic and graphic languages for OLAP manipulations. Int J Data Warehous Min 4(1):17–46CrossRefGoogle Scholar
  21. Rehman N, Mansmann S, Weiler A, Scholl M.H (2012) Building a data warehouse for twitter stream exploration. In: ACM fifteenth international workshop on data warehousing and OLAP, DOLAPGoogle Scholar
  22. Rehman N, Weiler A, Scholl MH (2013) OLAPing social media: the case of twitter. In: IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM 2013)Google Scholar
  23. Sakaki T, Okazaki M, Matsuo Y (2013) Earthquake shakes twitter users: real-time event detection by social sensors. In: IEEE computer society, vol 25, Issue 4, Apr 2013Google Scholar
  24. Twitter Team (2012) Twitter turns six [Online]. http://blog.twitter.com/2012/03/twitter-turns-six.html
  25. Vassiliadis P (1999) A survey of logical models for OLAP databases. ACM SIGMOD Record 28(4):64–69CrossRefGoogle Scholar
  26. Vosecky J, Jiang D, Leung KWT, Ng W (2013). Dynamic multi-faceted topic discovery in twitter. In: Proceedings of the 22nd ACM international conference on information & knowledge management CIKM’13, pp 879–884Google Scholar

Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  • Maha Ben Kraiem
    • 1
    Email author
  • Jamel Feki
    • 1
  • Kaïs Khrouf
    • 1
  • Franck Ravat
    • 2
  • Olivier Teste
    • 2
  1. 1.MIR@CLUniversity of SfaxSfaxTunisia
  2. 2.IRITUniversity of ToulouseToulouse Cedex 9France

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