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Measuring Self-monitoring Using Facebook Online Data Based on Snyder’s Psychological Theories

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10638))

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Abstract

Measuring psychological concept self-monitoring (SM) is useful for understanding how people employ impression management strategies in their social interactions. Recently, researchers have attempted to utilize the online user data to measure users’ SM value. However, in earlier researches, self-monitoring individuals’ specific behavioral and psychological characteristics haven’ t been sufficiently considered in the process of features extraction. In this paper, motivated by psychologist Snyder’s SM psychological theories, we propose to extract the behavior character of self-monitoring individuals in social network at the macro-level to measure SM. Besides, some other SM relevant features, situational factors, implicit topic words in status updates and demographics are also extracted. Furthermore, a new SM measuring method is presented by exploiting various kinds of users’ online data. The experimental results on a benchmark dataset show that all these features are effective and our SM measuring method can outperform many baseline methods.

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Acknowledgement

We are grateful to David Stillwell and Michal Kosinski for sharing myPersonality project data with us. The research is supported by the National Key Research and Development Program of China (No. 2016YFB0800402); the National Natural Science Foundation of China (No. U1536201).

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Authors and Affiliations

  1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China

    Ying Liu

  2. Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing, China

    Yongfeng Huang & Xuanmei Qin

Authors
  1. Ying Liu
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  2. Yongfeng Huang
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  3. Xuanmei Qin
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Corresponding author

Correspondence to Yongfeng Huang .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Liu, Y., Huang, Y., Qin, X. (2017). Measuring Self-monitoring Using Facebook Online Data Based on Snyder’s Psychological Theories. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_92

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  • DOI: https://doi.org/10.1007/978-3-319-70139-4_92

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70138-7

  • Online ISBN: 978-3-319-70139-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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