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Conscientiousness Measurement from Weibo’s Public Information

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Partially Supervised Learning (PSL 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8183))

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Abstract

We apply a graph-based semi-supervised learning algorithm to identify the conscientiousness of Weibo users. Given a set of Weibo users’ public information (e.g., number of followers) and a few labeled Weibo users, the task is to predict conscientiousness assessment for numeric unlabeled Weibo users. Singular value decomposition (SVD) technique is taken for feature reduction, and K nearest neighbor (KNN) method is used to recover a sparse graph. The local and global consistency algorithm is followed to deal with our data. Experiments demonstrate the advantage of semi-supervised learning over standard supervised learning when limited labeled data are available.

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Notes

  1. 1.

    http://open.weibo.com

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Acknowledgments

The authors gratefully acknowledge the generous support from National High-tech R&D Program of China (2013AA01A606), NSFC (61070115), Institute of Psychology (113000C037), Strategic Priority Research Program (XDA06030800) and 100-Talent Project (Y2CX093006) from Chinese Academy of Sciences.

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Authors and Affiliations

  1. Institute of Psychology, University of Chinese Academy of Sciences, 100190, CAS, Beijing, China

    Dong Nie, Lin Li & Tingshao Zhu

Authors
  1. Dong Nie
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  2. Lin Li
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  3. Tingshao Zhu
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Corresponding author

Correspondence to Tingshao Zhu .

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Editors and Affiliations

  1. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, People's Republic of China

    Zhi-Hua Zhou

  2. Abt. Neuroinformatik, Universität Ulm, Ulm, Germany

    Friedhelm Schwenker

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Nie, D., Li, L., Zhu, T. (2013). Conscientiousness Measurement from Weibo’s Public Information. In: Zhou, ZH., Schwenker, F. (eds) Partially Supervised Learning. PSL 2013. Lecture Notes in Computer Science(), vol 8183. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40705-5_6

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  • DOI: https://doi.org/10.1007/978-3-642-40705-5_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40704-8

  • Online ISBN: 978-3-642-40705-5

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