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A Noise-Filtering Approach for Spatio-temporal Event Detection in Social Media

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Advances in Information Retrieval (ECIR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9022))

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Abstract

We propose an iterative spatial-temporal mining algorithm for identifying and extracting events from social media. One of the key aspects of the proposed algorithm is a signal processing-inspired approach for viewing spatial-temporal term occurrences as signals, analyzing the noise contained in the signals, and applying noise filters to improve the quality of event extraction from these signals. The iterative event mining algorithm alternately clusters terms and then generates new filters based on the results of clustering. Through experiments on ten Twitter data sets, we find improved event retrieval compared to two baselines.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA

    Yuan Liang, James Caverlee & Cheng Cao

Authors
  1. Yuan Liang
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  2. James Caverlee
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  3. Cheng Cao
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Editor information

Editors and Affiliations

  1. Vienna University of Technology, Institute of Software Technology and Interactive Systems, Favoritenstraße 9-11/188, 1040, Vienna, Austria

    Allan Hanbury

  2. Lumi, Semion Ltd., 111 Charterhouse Street, EC1M 6AW, London, UK

    Gabriella Kazai

  3. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Favoritenstraße 9-11/188, 1040, Vienna, Austria

    Andreas Rauber

  4. Universität Duisburg-Essen, Lotharstraße 65, 47057, Duisburg, Germany

    Norbert Fuhr

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© 2015 Springer International Publishing Switzerland

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Liang, Y., Caverlee, J., Cao, C. (2015). A Noise-Filtering Approach for Spatio-temporal Event Detection in Social Media. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_25

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  • DOI: https://doi.org/10.1007/978-3-319-16354-3_25

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16353-6

  • Online ISBN: 978-3-319-16354-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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