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Extending Independent Component Analysis for Event Detection on Online Social Media

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 11314)

Abstract

In this paper, we propose a new approach for filtering noises from social event signals by using independent component analysis technique. With a case study about two practical events, we prove that our approach is feasible. Besides, this idea is also to acquire high adaptability and extensibility property because we use only temporal signals of social events as inputs without requiring any more information. Moreover, our approach can be used as pre-processing step for other signal-based event discovering algorithms.

Keywords

  • Social event
  • Independent component analysis
  • Noise reduction
  • Signal separation

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  • DOI: 10.1007/978-3-030-03493-1_82
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Notes

  1. 1.

    http://www.internetlivestats.com/.

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Acknowledgment

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2017R1A4A1015675, NRF-2018K1A-3A1A38056595).

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Correspondence to Jason J. Jung .

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Nguyen, H.L., Jung, J.J. (2018). Extending Independent Component Analysis for Event Detection on Online Social Media. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_82

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  • DOI: https://doi.org/10.1007/978-3-030-03493-1_82

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