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A Hybrid Feature Selection Method for Predicting User Influence on Twitter

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Web Information Systems Engineering – WISE 2015 (WISE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9418))

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Abstract

This paper proposes a hybrid feature selection method for predicting user influence on Twitter. A set of candidate features from Twitter is identified based on the five attributes of influencers defined in sociology. Firstly, less relevant features are filtered out with a feature-weighting algorithm. Then the Sequential Backward Floating Selection is utilized as the search strategy. A Back Propagation Neural Network is employed to evaluate the feature subset at each step of searching. Finally, an optimal feature set is obtained for predicting user influence with a high degree of accuracy. Experimental results are provided based on a real world Twitter dataset including seven million tweets associated with 200 popular users. The proposed method can provide a set of features that could be used as a solid foundation for studying complicated user influence evaluation and prediction.

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Notes

  1. 1.

    http://www.merriam-webster.com/.

  2. 2.

    https://klout.com/.

  3. 3.

    http://kred.com/.

  4. 4.

    http://www.peerindex.net/.

  5. 5.

    https://followerwonk.com/.

  6. 6.

    http://nlp.stanford.edu/software/tmt/tmt-0.4/.

  7. 7.

    http://www.cs.waikato.ac.nz/ml/weka/.

  8. 8.

    http://www.mathworks.com/products/neural-network/.

  9. 9.

    https://dev.twitter.com/rest/public.

  10. 10.

    https://dev.twitter.com/streaming/overview.

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Acknowledgments

The work presented in this paper was partially supported by Macquarie University Research Excellence Scholarship (Allocation No.2013115), an Australian Research Council Linkage Project (LP120200231) and the China Scholarship Council. We also thank anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. Macquarie University, Sydney, Australia

    Yan Mei, Weiliang Zhao, Jian Yang & Robertus Nugroho

  2. University of International Business and Economics, Beijing, China

    Zizhu Zhang

Authors
  1. Yan Mei
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  2. Zizhu Zhang
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  3. Weiliang Zhao
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  4. Jian Yang
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  5. Robertus Nugroho
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Corresponding author

Correspondence to Yan Mei .

Editor information

Editors and Affiliations

  1. Tsinghua University, Bijing, China

    Jianyong Wang

  2. Poznan University of Economics, Poznan, Poland

    Wojciech Cellary

  3. Florida Atlantic University, Boca Raton, Florida, USA

    Dingding Wang

  4. Victoria University, Melbourne, Australia

    Hua Wang

  5. School of Computing & Information, Florida International University, Miami, Florida, USA

    Shu-Ching Chen

  6. Florida International University, Miami, Florida, USA

    Tao Li

  7. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

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© 2015 Springer International Publishing Switzerland

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Mei, Y., Zhang, Z., Zhao, W., Yang, J., Nugroho, R. (2015). A Hybrid Feature Selection Method for Predicting User Influence on Twitter. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9418. Springer, Cham. https://doi.org/10.1007/978-3-319-26190-4_32

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  • DOI: https://doi.org/10.1007/978-3-319-26190-4_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26189-8

  • Online ISBN: 978-3-319-26190-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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