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Predicting Personality Traits of Users in Social Networks

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Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

Abstract

With the popularity of social media, an emerging interest is on predicting personality by mining users’ digital footprints. Some researches have proven that social behaviors of users in social network are strongly influenced by their personality. However, current methods are mainly focused on selecting better features from user behaviors and then utilizing a classical classification model to separately predict different personality traits. Based on our observation and statistic analysis from the data, this paper proposes a unified semi-supervised method, Personality-dependent Variable Factor Graph model (PVFG), by considering not only the interrelations between a person’s personality traits, but also the interrelations with their friends’ personality traits. The experiment is carried on two real-world datasets and the results indicate that the proposed method outperforms several alternative methods.

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Notes

  1. 1.

    http://mypersonality.org/.

  2. 2.

    http://realitycommons.media.mit.edu/.

  3. 3.

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

  4. 4.

    https://www.csie.ntu.edu.tw/~cjlin/libsvm/.

  5. 5.

    http://meka.sourceforge.net/.

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Author information

Authors and Affiliations

  1. Institute of Software Chinese Academy of Sciences, 4# South Fourth Street, ZhongGuanCun, Beijing, 100190, People’s Republic of China

    Zhili Ye, Yang Du & Li Zhao

  2. University of Chinese Academy of Sciences, 80# ZhongGuanCun East Road, Beijing, 100190, People’s Republic of China

    Zhili Ye, Yang Du & Li Zhao

Authors
  1. Zhili Ye
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  2. Yang Du
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  3. Li Zhao
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Corresponding author

Correspondence to Zhili Ye .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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Ye, Z., Du, Y., Zhao, L. (2017). Predicting Personality Traits of Users in Social Networks. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_21

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  • DOI: https://doi.org/10.1007/978-3-319-68935-7_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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