Sensing Subjective Well-Being from Social Media

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.

[1]  Johan Bollen,et al.  Twitter Mood as a Stock Market Predictor , 2011, Computer.

[2]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[3]  R. Craig,et al.  Assessing Personality and Psychopathology With Interviews , 2003 .

[4]  Stephen Wu,et al.  Objective Confirmation of Subjective Measures of Human Well-Being: Evidence from the U.S.A. , 2010, Science.

[5]  Shiro Usui,et al.  Brain and Health Informatics , 2013, Lecture Notes in Computer Science.

[6]  Adam D. I. Kramer.  An unobtrusive behavioral model of "gross national happiness" , 2010, CHI.

[7]  Margaret L. Kern,et al.  Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach , 2013, PloS one.

[8]  J. Stiglitz,et al.  Mismeasuring our lives : why GDP doesn't add up : the report by the Commission on the Measurement of Economic Performance and Social Progress , 2010 .

[9]  J. Stiglitz,et al.  Report by the commission on the measurement of economic performance and social progress , 2011 .

[10]  Daniele Quercia,et al.  The personality of popular facebook users , 2012, CSCW.

[11]  Gerald C. Davison,et al.  American Psychological Association (APA) , 2015 .

[12]  Eric Horvitz,et al.  Predicting Depression via Social Media , 2013, ICWSM.

[13]  Winton Bates,et al.  Gross National Happiness , 2009 .

[14]  C. Keyes,et al.  The measurement and utility of adult subjective well-being. , 2003 .

[15]  Lin Li,et al.  Predicting Active Users' Personality Based on Micro-Blogging Behaviors , 2014, PloS one.

[16]  C. Keyes,et al.  The structure of psychological well-being revisited. , 1995, Journal of personality and social psychology.

[17]  Cameron Marlow,et al.  Social network activity and social well-being , 2010, CHI.

[18]  He Li,et al.  Developing Simplified Chinese Psychological Linguistic Analysis Dictionary for Microblog , 2013, Brain and Health Informatics.

[19]  Christopher M. Danforth,et al.  Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter , 2011, PloS one.

[20]  Richard E. Lucas,et al.  Subjective Weil-Being: Three Decades of Progress , 2004 .

[21]  D. Watson,et al.  Development and validation of brief measures of positive and negative affect: the PANAS scales. , 1988, Journal of personality and social psychology.

[22]  R. Larsen,et al.  The Satisfaction with Life Scale , 1985, Journal of personality assessment.

[23]  J. Mayer,et al.  Convergent, Discriminant, and Incremental Validity of Competing Measures of Emotional Intelligence , 2003, Personality & social psychology bulletin.

[24]  C. Keyes,et al.  The structure of psychological well-being revisited , 1995 .

[25]  Lin Li,et al.  Predicting Mental Health Status on Social Media - A Preliminary Study on Microblog , 2013, HCI.

[26]  J. Pennebaker,et al.  PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES Words of Wisdom: Language Use Over the Life Span , 2003 .

[27]  Angela L. Duckworth,et al.  A Meta-Analysis of the Convergent Validity of Self-Control Measures. , 2011, Journal of research in personality.

[28]  Megha Agrawal,et al.  Characterizing Geographic Variation in Well-Being Using Tweets , 2013, ICWSM.