Data Mining and Knowledge Discovery

, Volume 29, Issue 5, pp 1374–1405 | Cite as

Multiscale event detection in social media

  • Xiaowen Dong
  • Dimitrios Mavroeidis
  • Francesco Calabrese
  • Pascal Frossard


Event detection has been one of the most important research topics in social media analysis. Most of the traditional approaches detect events based on fixed temporal and spatial resolutions, while in reality events of different scales usually occur simultaneously, namely, they span different intervals in time and space. In this paper, we propose a novel approach towards multiscale event detection using social media data, which takes into account different temporal and spatial scales of events in the data. Specifically, we explore the properties of the wavelet transform, which is a well-developed multiscale transform in signal processing, to enable automatic handling of the interaction between temporal and spatial scales. We then propose a novel algorithm to compute a data similarity graph at appropriate scales and detect events of different scales simultaneously by a single graph-based clustering process. Furthermore, we present spatiotemporal statistical analysis of the noisy information present in the data stream, which allows us to define a novel term-filtering procedure for the proposed event detection algorithm and helps us study its behavior using simulated noisy data. Experimental results on both synthetically generated data and real world data collected from Twitter demonstrate the meaningfulness and effectiveness of the proposed approach. Our framework further extends to numerous application domains that involve multiscale and multiresolution data analysis.


Multiscale event detection Spatiotemporal analysis  Wavelet decomposition Modularity-based clustering 



X. Dong is supported by a Swiss National Science Foundation Mobility Fellowship. This work was done while X. Dong and D. Mavroeidis were at IBM Research - Ireland.


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Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Xiaowen Dong
    • 1
  • Dimitrios Mavroeidis
    • 2
  • Francesco Calabrese
    • 3
  • Pascal Frossard
    • 4
  1. 1.MIT Media LabCambridgeUSA
  2. 2.Philips ResearchEindhovenNetherlands
  3. 3.IBM ResearchDublinIreland
  4. 4.École Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

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