Journal of University of Chinese Academy of Sciences >
Depression and anxiety prediction on microblogs
Received date: 2013-11-06
Revised date: 2014-02-24
Online published: 2014-11-15
Supported by
Supported by NSFC(61070115),Strategic Priority Research Program(XDA06030800) and 100-Talent Project(Y2CX093006) from CAS
As generally accepted in clinical psychology,mental health status can be expressed by behavior,including web behavior. Conventional mental health assessment is performed by self-report inventory, and it requires much manual efforts and cannot be done in real time. We aim to objectively predict user's mental health status,especially depression and anxiety. We confirmed the correlation between personality and mental health status in the conventional theory by examining a set of behavior data on sina microblog environment. Multi-task regression is proposed to predict online user's mental health status. The results indicate that mental health disorders are expressed by specific online behaviors and it is possible to predict user's degree of depression and anxiety through his/her microblog usage.
Key words： depression; anxiety; sina microblog
BAI Shuotian , HAO Bibo , LI Ang , NIE Dong , ZHU Tingshao . Depression and anxiety prediction on microblogs[J]. Journal of University of Chinese Academy of Sciences, 2014 , 31(6) : 814 -820 . DOI: 10.7523/j.issn.2095-6134.2014.06.013
[1] Herrman H,Saxena S,Moodie R. Promoting mental health:concepts,emerging evidence,practice[M]. Geneva: World Health Organization,2005: 12.
[2] Kitchener B A,Jorm A F,Kelly D C. Mental health first aid manual[M]. Canberra: Centre for Mental Health Research,The Australian National University,2002: 10.
[3] Carpenter K M,Hasin D S,Allison D B,et al. Relationships between obesity and DSM-IV major depressive disorder,suicide ideation,and suicide attempts: results from a general population study[J]. American Journal of Public Health,2000,90(2): 251.
[4] Ahn Y Y,Han S,Kwak H,et al. Analysis of topological characteristics of huge online social networking services[C]//Proceedings of the 16th International Conference on World Wide Web. ACM,2007: 835-844.
[5] Amiel T,Sargent S L. Individual differences in Internet usage motives[J]. Computers in Human Behavior,2004,20(6): 711-726.
[6] Goby V P. Personality and online/offline choices: MBTI profiles and favored communication modes in a Singapore study[J]. CyberPsychology & Behavior,2006,9(1): 5-13.
[7] Furnham A,Brewin C R. Personality and happiness[J]. Personality and Individual Differences,1990,11(10): 1 093-1 096.
[8] Matthews G,Deary I J,Whiteman M C. Personality traits[M]. Oxford: Cambridge University Press,2003.
[9] Ozer D J,Benet-Martinez V. Personality and the prediction of consequential outcomes[J]. Annual Review of Psychology,2006,57: 401-421.
[10] Gosling S D,Augustine A A,Vazire S,et al. Manifestations of personality in online social networks: self-reported facebook-related behaviors and observable profile information[J]. CyberPsychology,Behavior,and Social Networking,2011,14(9): 483-488.
[11] Campbell A J,Cumming S R,Hughes I. Internet use by the socially fearful: addiction or therapy?[J]. CyberPsychology & Behavior,2006,9(1): 69-81.
[12] Bessière K,Kiesler S,Kraut R,et al. Effects of Internet use and social resources on changes in depression[J]. Information,Community & Society,2008,11(1): 47-70.
[13] Correa T,Hinsley A W,De Zuniga H G. Who interacts on the Web?: the intersection of users' personality and social media use[J]. Computers in Human Behavior,2010,26(2): 247-253.
[14] Yang S C,Tung C J. Comparison of Internet addicts and non-addicts in Taiwanese high school[J]. Computers in Human Behavior,2007,23(1): 79-96.
[15] Kosinski M,Stillwell D,Graepel T. Private traits and attributes are predictable from digital records of human behavior[J]. Proceedings of the National Academy of Sciences,2013,110(15): 5 802-5 805.
[16] Schwartz H A,Eichstaedt J C,Kern M L,et al. Personality,gender,and age in the language of social media: the open-vocabulary approach[J]. PLoS One,2013,8(9): e73791.
[17] Derogatis L R. SCL-90: administration,scoring and procedures manual-I for the R (evised) version and other instruments of the psychopathology rating scale series. Baltimore: John Hopkins University,1977.
[18] Argyriou A,Evgeniou T,Pontil M. Convex multi-task feature learning[J]. Machine Learning,2008,73(3): 243-272.
[19] Bishop C M. Pattern recognition and machine learning[M]. New York: Springer,2006.
[20] Furnham A,Cheng H. Personality as predictor of mental health and happiness in the East and West[J]. Personality and Individual Differences,1999,27(3): 395-403.
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