Time-Frequency Social Data Analytics for Understanding Social Big Data

Part of the Studies in Computational Intelligence book series (SCI, volume 570)

Abstract

Social Network Services (SNS) have been the most popular channel where users can generate and disseminate a large amount of information (so-called ‘social big data’) among other users efficiently. Discovering meaningful patterns from these SNS (e.g., clustering relevant messages, detecting events, and understanding trends of social communities) is an important, but difficult research issue on social big data analytics. In this paper, we present an on-going work to transform social data in time domain to in frequency domain for detecting meaningful events from the social big data. Consequently, this work is expected to significantly reduce the volume (and also, complexity) of the social data and to improve the performance of the data analytics.

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References

  1. 1.
    Aggarwal, C.C., Subbian, K.: Event detection in social streams. In: Proceedings of the 12th SIAM International Conference on Data Mining, Anaheim, California, USA, April 26-28, pp. 624–635 (2012)Google Scholar
  2. 2.
    Aiello, L.M., Petkos, G., Martín, C.J., Corney, D., Papadopoulos, S., Skraba, R., Göker, A., Kompatsiaris, I., Jaimes, A.: Sensing trending topics in twitter. IEEE Transactions on Multimedia 15(6), 1268–1282 (2013)CrossRefGoogle Scholar
  3. 3.
    He, Q., Chang, K., Lim, E.P.: Analyzing feature trajectories for event detection. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007, pp. 207–214. ACM (2007)Google Scholar
  4. 4.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)Google Scholar
  5. 5.
    Nguyen, D.T., Jung, J.E.: Privacy-preserving discovery of topic-based events from social sensor signals: An experimental study on twitter. The Scientific World Journal 2014, Article ID 204785 (2014)Google Scholar
  6. 6.
    Weng, J., Lee, B.S.: Event detection in twitter. In: Proceedings of the Fifth International Conference on Weblogs and Social Media, ICWSM 2011, Barcelona, Catalonia, Spain. The AAAI Press (2011)Google Scholar
  7. 7.
    Zhou, X., Chen, L.: Event detection over twitter social media streams. VLDB Journal 23(3), 381–400 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Yeungnam UniversityGyeongsanKorea
  2. 2.Chung-Ang UniversitySeoulKorea

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