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Learning Event Profile for Improving First Story Detection in Twitter Stream

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Web Information Systems Engineering – WISE 2016 (WISE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10041))

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Abstract

First Story Detection (FSD) in twitter stream is to identify the first report that discusses an event that has not been reported in the posted tweets. FSD offers great assistance for New Event Detection (NED). Traditional methods used online clustering framework as mainstream solutions, but suffering low efficiency and unsatisfied performance and did not consider the event related features. We merge event related features and propose event-profile based FSD method based on online cluster framework. It outperforms traditional methods both in efficiency and effect by replacing tweet-by-tweet comparison with profile-by-profile comparison. In this paper, we take four groups of features into account and propose a learning method for the generation of event profile. Experiments show that the profile produced by our method is more relevant with event, also more robust than the ones produced by rule-based methods, eventually, improves the FSD performance.

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Notes

  1. 1.

    http://www.ebizmba.com/articles/social-networking-websites.

  2. 2.

    ftp://jaguar.ncsl.nist.gov/current_docs/TDT3eval/TDT3fsd.pl.

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Acknowledgments

We would like to thank the anonymous reviewers for their valuable comments and suggestions. This work is supported by the National Natural Science Foundation of China (grant No. 61572494), the Strategic Priority Research Program of the Chinese Academy of Sciences (grant No. XDA06030200), and the National Key Technology R and D Program (grant No. 2012BAH46B03).

Author information

Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Yongqin Qiu, Rui Li, Lihong Wang & Bin Wang

  2. University of Chinese Academy of Sciences, Beijing, China

    Yongqin Qiu, Rui Li, Lihong Wang & Bin Wang

Authors
  1. Yongqin Qiu
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  2. Rui Li
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  3. Lihong Wang
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  4. Bin Wang
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Corresponding author

Correspondence to Rui Li .

Editor information

Editors and Affiliations

  1. Poznań University of Economics, Poznan, Poland

    Wojciech Cellary

  2. University of Minnesota, Minneapolis, Minnesota, USA

    Mohamed F. Mokbel

  3. Tsinghua University, Beijing, China

    Jianmin Wang

  4. Victoria University, Melbourne, Victoria, Australia

    Hua Wang

  5. Victoria University, Melbourne, Victoria, Australia

    Rui Zhou

  6. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

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© 2016 Springer International Publishing AG

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Qiu, Y., Li, R., Wang, L., Wang, B. (2016). Learning Event Profile for Improving First Story Detection in Twitter Stream. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_37

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  • DOI: https://doi.org/10.1007/978-3-319-48740-3_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48739-7

  • Online ISBN: 978-3-319-48740-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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