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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)

Abstract

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.

Keywords

Subjective Well-being Social Media Machine Learning 

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References

  1. 1.
    Kosinskia, M., Stillwella, D., Graepelb, T.: Private traits and attributes are predictable from digital records of human behavior. PNAS 110(15), 5802–5850 (2013)CrossRefGoogle Scholar
  2. 2.
    Li, L., Li, A., Hao, B., Guan, Z., Zhu, T.: Predicting active users’ personality based on micro-blogging behaviors. PloS One 9(1), e84997 (2014)Google Scholar
  3. 3.
    Schwartz, H.A., Eichstaedt, J.C., et al.: Personality, gender, and age in the language of social media: The open-vocabulary approach. PLoS ONE 8(9), e73791 (2013)Google Scholar
  4. 4.
    Keyes, C.L.M., Magyar-Moe, J.L.: The measurement and utility of adult subjective well-being. In: Positive Psychological Assessment. American Psychological Association, pp. 411–425 (2003)Google Scholar
  5. 5.
    Oswald, A.J., Wu, S.: Objective confirmation of subjective measures of human well-being: Evidence from the usa. Science 327(5965), 576–579 (2010)CrossRefGoogle Scholar
  6. 6.
    Stiglitz, J.E., Sen, A., Fitoussi, J.P., et al.: Report by the commission on the measurement of economic performance and social progress. Paris: Commission on the Measurement of Economic Performance and Social Progress (2010)Google Scholar
  7. 7.
    OECD: OECD Guidelines on Measuring SubjectiveWell-being. OECD Publishing (2013), http://dx.doi.org/10.1787/9789264191655-en
  8. 8.
    Schwartz, H.A., Eichstaedt, J.C., Kern, M.L., Dziurzynski, L., Agrawal, M., Park, G.J., Lakshmikanth, S.K., Jha, S., Seligman, M.E., Ungar, L., et al.: Characterizing geographic variation in well-being using tweets. In: Seventh International AAAI Conference on Weblogs and Social Media, ICWSM 2013 (2013)Google Scholar
  9. 9.
    Watson, D., Clark, L.A.: Development and validation of brief measures of positive and negative affect: The panas scales. Journal of Personality and Social Psychology 54(6), 719–727 (1998)Google Scholar
  10. 10.
    Ryff, C.D., Keyes, C.L.M.: The structure of psychological well-being revisited. Journal of Personality and Social Psychology 69(4), 719–727 (1995)CrossRefGoogle Scholar
  11. 11.
    Pennebaker, J.W., Stone, L.D.: Words of wisdom: Language use over the life span. Journal of Personality and Social Psychology 85(2), 291–301 (2003)CrossRefGoogle Scholar
  12. 12.
    Dodds, P.S., Harris, K.D., Kloumann, I.M., Bliss, C.A., Danforth, C.M.: Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLoS ONE 6(12), e26752 (2011)Google Scholar
  13. 13.
    Bollen, J., Mao, H.: Twitter mood as a stock market predictor. IEEE Computer 44(10), 91–94 (2011)CrossRefGoogle Scholar
  14. 14.
    Kramer, A.D.I.: An unobtrusive behavioral model of “gross national happiness”. In: CHI, pp. 287–290 (2010)Google Scholar
  15. 15.
    Diener, E., Emmons, R.A., Larsen, R.J., Griffin, S.: The satisfaction with life scale. Journal of Personality Assessment 49, 71–75 (1985)CrossRefGoogle Scholar
  16. 16.
    Burke, M., Marlow, C., Lento, T.: Social network activity and social well-being. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1909–1912. ACM (2010)Google Scholar
  17. 17.
    Quercia, D., Lambiotte, R., Stillwell, D., Kosinski, M., Crowcroft, J.: The personality of popular facebook users. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, pp. 955–964. ACM (2012)Google Scholar
  18. 18.
    Hao, B., Li, L., Li, A., Zhu, T.: Predicting mental health status on social media. In: Rau, P.L.P. (ed.) CCD/HCII 2013, Part II. LNCS, vol. 8024, pp. 101–110. Springer, Heidelberg (2013)Google Scholar
  19. 19.
    De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: AAAI Conference on Weblogs and Social Media (2013)Google Scholar
  20. 20.
    Brackett, M.A., Mayer, J.D.: Convergent, discriminant, and incremental validity of competing measures of emotional intelligence. Personality and Social Psychology Bulletin 29(9), 1147–1158 (2003)CrossRefGoogle Scholar
  21. 21.
    Duckworth, A.L., Kern, M.L.: A meta-analysis of the convergent validity of self-control measures. Journal of Research in Personality 45(3), 259–268 (2011)CrossRefGoogle Scholar
  22. 22.
    Graham, J.R.: Assessing personality and psychopathology with interviews. In: Handbook of Psychology: Assessment Psychology, vol. 10, p. 487 (2003)Google Scholar
  23. 23.
    Diener, E., Suh, E.M., Lucas, R.E., Smith, H.L.: Subjective well-being: Three decades of progress. Psychological Bulletin 125(2), 276 (1999)CrossRefGoogle Scholar
  24. 24.
    Gao, R., Hao, B., Li, H., Gao, Y., Zhu, T.: Developing simplified chinese psychological linguistic analysis dictionary for microblog. In: Imamura, K., Usui, S., Shirao, T., Kasamatsu, T., Schwabe, L., Zhong, N. (eds.) BHI 2013. LNCS, vol. 8211, pp. 359–368. Springer, Heidelberg (2013)CrossRefGoogle Scholar

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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