Sensing Subjective Well-Being from Social Media

  • Bibo Hao
  • Lin Li
  • Rui Gao
  • Ang Li
  • Tingshao Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8610)


Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users’ social media data with SWB labels, we train machine learning models that are able to “sense” individual SWB. Our model, which attains the state-of-the-art prediction accuracy, can then be applied to identify large amount of social media users’ SWB in time with low cost.


Subjective Well-being Social Media Machine Learning 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bibo Hao
    • 1
  • Lin Li
    • 2
  • Rui Gao
    • 1
  • Ang Li
    • 1
  • Tingshao Zhu
    • 1
  1. 1.Institute of PsychologyUniversity of Chinese Academy of Sciences, CASChina
  2. 2.School of Humanities and Social SciencesNanyang Technological UniversitySingapore

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